Land degradation and food security nexus in Sub Saharan Africa

Land degradation and food security nexus in Sub Saharan Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Land degradation and food security nexus in Sub Saharan Africa Ebrima K. Ceesay, Kadio Valere, Alasana Gitteh, Mohamed Ben Omar Ndiaye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4709288/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: This article explores the relationship between land degradation and food security in Sub-Saharan African countries, shedding light on the critical issues faced in the region. Land degradation, caused by factors such as poor rainfall, deforestation, erosion, and other major causes, significantly impacts the fertility of the soil, leading to food security challenges. Understanding the impact of desertification, poor rainfall, drought, and extreme climate change in Africa is crucial to addressing food security problems in the region. Method: The study utilizes data from the World Development Indicators and employs instrumental variable estimation (IV), panel OLS and pooled OLS methods to analyze the relationship between food production (as a proxy for food security) and various independent variables, including arable land area, fertilizer consumption, agricultural irrigated land area, and average precipitation depth. Results: The findings reveal three different types of regression analyses. The first analysis was to Instrumental variable estimation (IV). When we used rainfall and GHG as an instrument for land productivity, proxy land degradation, we found that land productivity and cereal yields increases food productions, proxy food security. The second analysis, a random-effects Generalizing least square regression, indicates that fertilizer consumption and average precipitation depth are significant predictors of food production. However, arable land area and agricultural irrigated land area do not significantly impact food production. Interestingly, agricultural irrigated land shows a positive effect on food security in Sub-Saharan African countries, while arable land (as a proxy for land degradation) has a negative impact on food security in the region. The third analysis, a multiple linear regression, supports the results of the Generalizing least square regression, demonstrating that fertilizer consumption and average precipitation depth are significant predictors of food production. However, arable land area do not significantly influence food production. Remarkably, agricultural irrigated land is found to be a positive predictor of food production and serves as a proxy for food security. Discussion: In conclusion, this study highlights the detrimental impact of land degradation on food security in Sub-Saharan African countries. It emphasizes the significance of factors such as fertilizer consumption, land productivity-proxy land degradation, cereal yields, Greenhouse gas emission, average precipitation depth, and the role of agricultural irrigated land in addressing food security challenges in the region. Instrumental variables Sub-Saharan Africa Panel OLS Land productivity Greenhouse gas emission Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Land degradation is defined as "a negative trend in land conditions caused by direct or indirect human-induced processes, including anthropogenic climate change, expressed as long-term reduction or loss of at least one of the following aspects: biological productivity, ecological integrity, or value to humans" (IPCC, 2019). Another definition by FAO, land degradation is defined as a change in the soil health status resulting in a diminished capacity of the ecosystem to provide goods and services for its beneficiaries (FAO, 2014). Hunger: craving or urgent need for food or a specific nutrient. In other word, an uneasy sensation occasioned by the lack of food, Finally, hunger is a weakened condition brought about by prolonged lack of food (https://www.merriam-webster.com/dictionary/hunger) Hunger is an uncomfortable or painful physical sensation caused by insufficient consumption of dietary energy( FAO,2024, (World Bank, 1986)) (Clay, 2002). Food security: exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food favorites for an energetic and healthy lifetime (World Food Summit 1996) (FAO 2008). Food insecurity : A person is food insecure when they lack regular access to enough safe and nutritious food for normal growth and development and an active and healthy life. This may be due to unavailability of food and/or lack of resources to obtain food(FAO, 2020). Land degradation poses a significant challenge to agricultural productivity and food availability in Sub-Saharan Africa. Depending on the extent of the degradation, it could experience a temporary or permanent decline in its ability to support the human food supply (Gupta, 2019). Indeed, if land fertility is more pronounced, food supply disruption will become more frequent posing the issue of food insecurity in Sub-saharan Africa. According to (https://www.worldvision.org/hunger-news-stories/africa, 2022, FAO 2022), an estimated 20% of the population is undernourished in Africa, with 57 million more people facing hunger since the start of the COVID-19 pandemic. Further, they estimated 868 million people experienced moderate to severe food insecurity in Africa in 2022, with over one-third of those facing severe food insecurity According to World Bank, 2022, at least one in five Africans goes to bed hungry and an estimated 140 million people in Africa face acute food insecurity, according to the 2022 Global Report on Food Crises 2022 Mid-Year Update. According to WFP, 2024, Nearly 55 million people in West and Central Africa will struggle to feed themselves in the June-August 2024 lean season, according to the March 2024 Cadre Harmonisé food security analysis released by the Permanent Inter-State Committee for Drought Control in the Sahel (CILSS). This are the mains problems of Africa according to (Wijnand Klaver, 2015). high population growth rates (a burden because the cake has to be divided among more people); a young population (which could potentially offer an economic dividend in the coming decades); a high percentage of food produced by women (many of whom are poor but very resourceful); a highly diverse ecology (which poses particular challenges in terms of an agricultural ‘revolution’); an increase in potential and current conflicts around competing claims (e.g. pastoralists versus farmers, food versus biofuel, water for food versus water for export flowers); microclimates and climate change (which will lead to certain areas becoming ‘bread baskets’ and negatively impact on others that will become virtually uninhabitable); evolving institutional and governance structures; and certain food habits (shaped by tradition but strongly influenced by cosmopolitan trends). According to IPBES (2018), it is caused by natural and anthropogenic direct drivers, which are influenced by indirect drivers. As natural causes driving land degradation, we can quote earthquakes, volcanic eruptions, hurricanes, floods, landslides, typhoons, and recurring outbreaks of pests and pathogens (IPBES, 2018). They happen episodically with periodicities ranging from years to millennia (Angaman & Niang, 2023). Concerning the anthropogenic direct causes, they are directly linked to human activities and range from local to regional or global scales posing serious threat to food security. According to Díaz et al. (2015), indirect drivers represent the fundamental root causes of land degradation. These drivers originate from how human societies operate, organize themselves, and engage with nature across various levels. Indirect drivers are typically external to the specific ecosystem being studied. As example, IPBES (2018) quotes demographic factors, economic activities, sciences, knowledge and technology, institutions and governance, and cultural aspect as indirect drivers of land degradation. Land degradation has a significant impact on food security. It reduces agricultural productivity, decreases crop quality, and limits access to arable land. It reduces also water availability and increases the prevalence of invasive species which are harmful to the growth of crops (Abdeta & Geleto, 2018). In this regard, land degradation should be stop in order to have good soil to cultivate crops for effective food production. Education, changes in policies, use of technology for meaningful innovation are vital to be considered to restoration of degraded land and future land degradation. The fertility of the soil is greatly diminished by land degradation, resulting in reduced agricultural productivity. Soil erosion, nutrient depletion, and the loss of soil organic matter impair the land's capacity to support crop growth, leading to decreased yields and availability of food. Several studies, such as those conducted by Chalise et al. (2019), Ceesay & Ben Omar Ndiaye (2022), and Perspectives (2023), have confirmed this correlation. Another consequence of land degradation is the reduced access to arable land. As land becomes degraded, farmers face limitations in finding suitable land for cultivation, which hinders their ability to produce enough food for themselves and their communities. The works of Makurira (2011), UNCTAD (2015), and Keringingo & Kayakayacı (2023) provide support for this claim.Livestock production and grazing areas are also affected by land degradation. Degraded pastures lead to a shortage of fodder for livestock, negatively impacting their health and productivity. As a result, the availability of animal-sourced food products such as meat and dairy is diminished, further contributing to food insecurity. This is contrary to the findings of Weber & Horst (2011), Ceesay et al. (2021), Feltran-Barbieri & Féres (2021), and Macheroum & Chenchouni (2022). Water resources are adversely affected by land degradation as well. Decreased water holding capacity of soils, increased runoff, and reduced groundwater recharge contribute to water scarcity and poor water quality. These factors hamper agricultural activities, limit irrigation, and lead to lower crop yields and food production. Studies conducted by WHO (2002), UN (2007), and Chemical Releases Associated With (n.d.) support these findings. Land degradation can also initiate a negative feedback loop, whereby degraded land contributes to climate change and climate variability. In turn, climate change exacerbates land degradation through more frequent and intense droughts, floods, and extreme weather events, further compromising agricultural productivity and food security. Similar conclusions have been drawn in the study by Midler (2022). Understanding the multifaceted relationship between land degradation and food security is crucial for devising comprehensive strategies to address these challenges. By considering the various impacts described above, policymakers and stakeholders can work towards sustainable land management practices that ensure long-term food security for Sub-Saharan African countries and beyond. Research question: What is the impact of land degradation on food security in Sub Saharan Africa? Objective: The overall objective of the empirical model is to examine the relationship between land degradation and food security indicators, such as crop yield, land productivity, GHGE (greenhouse gas emission), food production, and rainfall. 2. Literature review One theoretical model that has been used to analyze the impact of land degradation on food security is the Pressure-State-Response (PSR) framework and the Driver-Pressure-State-Impact-Response (DPSIR). This model proposes that human activities create pressures on the environment, which affect the state of the environment, and then lead to responses from society or the government. In the case of land degradation and food security, human activities such as deforestation, overgrazing, and excessive use of chemical fertilizers create pressures on the land, which can result in soil erosion, loss of fertility, and reduced crop yields. These changes in the state of the land can then lead to responses from society, such as reduced access to food, increased food prices, and food insecurity (Liu & Hao, 2017 ); (Wolfslehner & Vacik, 2008 ) and (Hazbavi et al., 2020 ) while The Driver-Pressure-State-Impact-Response (DPSIR) was promoted to show the cause–effect relationships between environmental and human activities. The framework was introduced in a report by Maxim, L., Spangenberg, J. H., & O’Connor, M. (2009) to help policy makers to understand the meaning of the information in indicator reports. Thus, PSR is related to DPSIR in a number of ways according to the following authors; For PSR, society then responds to these changes on the environment by instituting environmental and economic programmes and policies, which feedback to reduce or mitigate the pressures or repair the natural resource such as mining, energy etc. Thus, for DPSIR framework was developed in the late 1990s and proposed by the Organisation of Economic Co-operation and Development (OECD, 2003) as a means of structuring and organizing indicators that affect and causes the pressures on environment due to human activities such as pollution, plastic that affect the environment, fossil fuel energy etc in a way that is meaningful to decision makers. Overall, DSPIR was built on previous environmental frameworks, such as the Pressure-State-Response (PSR) (OECD, 1993) and the Driver-State-Response (DSR) (UN, 1996) to understand the effect-cause relationship of PSR. Subsequently, there is a significant amount of empirical literature on the impact of land degradation on food security. Many studies have used panel data analysis to examine the relationship between land degradation and food security, controlling for other factors such as agricultural inputs, infrastructure, and economic growth. For instance, a study by Asfaw et al. (2019) investigated the impact of soil erosion on food security in Ethiopia using panel data analysis. The study found that soil erosion significantly reduced food production, and the negative effect was more severe in areas with poor soil quality. Similarly, a study by Demeke et al. (2018) examined the relationship between land degradation and food security in Sub-Saharan Africa using panel data analysis. The study found that land degradation had a significant negative impact on food security, and suggested that efforts to combat land degradation could lead to improved food security.Other studies have also used econometric models to examine the impact of specific types of land degradation, such as desertification or deforestation, on food security. For example, a study by Bai et al. (2018) examined the impact of desertification on food security in China using a structural equation model. The study found that desertification had a significant negative impact on food security, and suggested that policies aimed at preventing desertification could help improve food security in the region. In most literature the countries selected by the Dutch government for development cooperation in the coming years, namely Benin, Burundi, Ethiopia, Ghana, Kenya, Mali, Mozambique, Rwanda, Sudan and Uganda. Those countries have fertilie land for agriculture, but still no developmet in agriculture and food security taking place and even in the rest of the African countries all have fertile land but still lower budget assigned to agriculture especially in the Gambia almost only 2 percent of the total budget for 2020. Overall, empirical studies provide robust evidence that land degradation has a negative impact on food security, and highlight the importance of sustainable land management practices to improve food security.One gap in the literature could be the lack of studies that investigate the impact of specific types of land degradation on food security. Many studies have focused on the overall impact of land degradation on food security, but there is a need for research that examines the effects of different types of land degradation such as soil erosion, desertification, and deforestation on food production and access to food. Additionally, there is a need for studies that explore the underlying mechanisms through which land degradation affects food security, as this could inform more targeted policy interventions to address the issue.The paper will explore the panel FIV estimations or this study by using econometrics approaches. The aim of this paper is to examine the links between land dagradation anc food security in selected African countries and the implications on the stability of the country in particular and Africa in general.The study is rarely seeing in which Africa as it will used more efficient panel IV model with indication of food production as a proxy for food security and land degradation dicnators such as productivity of the land.. This study is very significant in order to allow policy decision in both international and Africa stakeholders especially the channels of envrionmental management, agricultural sector on the implications of the rising food insecurity and hunger in the Africa over the time period. 3. Materials and methods Data Collection: We Gathered data on land degradation indicators-land productivity, land cover change, and food security indicators- cereal yields, food production-food availability proxy, at the appropriate spatial and temporal scales. All data gathered are secondary data from FAOSTAT and WDI. Variable Selection: We identify the key variables that represent land degradation and food security and control variables. These variables include Food production index (2014–2016 = 100), Average precipitation, land productivity, GHGE (greenhouse gas emission), Irrigation, Arable land, and fertilizer consumption. 3.1. Econometric Specifications 3.1.1. Instrumental variables estimation (IV) Panel IV estimation is a statistical technique used to estimate the causal relationship between an endogenous variable, dependent variable and a set of exogenous, independent variables and instrumental variables in a panel dataset. It is useful when there is worry about endogeneity and omitted variable bias. The general formula for panel IV estimation is: Y it = α + βX it + γZ it + U it where: Y it is the dependent variable for individual i at time t X it is the endogenous explanatory variable for individual i at time t Z it is a set of exogenous and instrumental variables for individual i at time t α, β, and γ are the intercept, coefficient of the endogenous variable, and coefficients of the exogenous and instrumental variables, respectively U it is the error term for individual i at time t To estimate this model using panel IV, we use the following steps: Check for the presence of endogeneity and omitted variable bias. Choose appropriate instruments for the endogenous variable. Estimate the first stage regression to obtain the predicted values of the endogenous variable. Check for the validity of the instruments by testing for the exogeneity of the instruments and the relevance of the first stage regression. Estimate the second stage regression using the predicted values of the endogenous variable and the exogenous and instrumental variables.The IV estimator provides consistent and unbiased estimates of the coefficients, and the Sargan test can be used to test for the validity of the instruments. 3.1.2. Panel OLS regression model The panel OLS regression equation can be written as: Y = β 0 + β 1 X 1 + α i + u Where Y is the dependent variable, X is the independent variable, α is the individual-specific effect, and u is the idiosyncratic error term. The individual-specific effect captures any unobserved factors (α i ) that are constant over time for each individual, such as innate ability or family background. The idiosyncratic error term captures any factors that are specific to each observation, such as measurement error or random shocks (u).To estimate the coefficients of the regression equation, we can use ordinary least squares (OLS) on the pooled dataset, which combines all the observations for all individuals and all time periods. The OLS estimator gives us the best linear unbiased estimates of the coefficients (BLUE).One way to interpret the estimated coefficient β 1 is as the effect of land degradation on food security, controlling for individual-specific factors and idiosyncratic factors. We can use this coefficient to predict the change in food security associated with a one-unit increase in land degradation, on average, across all individuals and all time periods. 3.1.3. Pooled OLS Linear regression models The pooled ols is divided into simple and multiple linear regression frameworks as: Simple Linear Regression: The simple linear regression model examines the relationship between a dependent variable (Y) and a single independent variable (X). Y = β 0 + β 1 X + ε In this equation: Y represents the dependent variable, which is the variable to be predicted or explained. X represents the independent variable, which is the variable used to predict or explain the dependent variable. β 0 is the y-intercept, which represents the value of Y when X is zero. Β 1 is the slope coefficient, which represents the change in Y associated with a one-unit change in X. ε is the error term, which accounts for the variability in Y that is not explained by the independent variable. Multiple regression analysis: is a statistical technique used to estimate the relationship between a dependent variable and multiple independent variables. The estimation process involves determining the coefficients of the independent variables that best fit the data and provide the best prediction of the dependent variable. The estimation in multiple regression analysis is typically done using the method of ordinary least squares (OLS). Specify the regression model: Start by specifying the regression model, including the dependent variable and the independent variables. The model is expressed as: Y = β₀ + β₁X₁ + β₂X₂ + ... + βₖXₖ + ε In this equation: Y is the dependent variable. X₁, X₂, ..., Xₖ are the independent variables. β₀, β₁, β₂, ..., βₖ are the coefficients to be estimated and as Y changes, what happen to 1 unit changes in Xs’. ε is the error term. 3.2. Empirical Model Specification: 3.2.1. IV We suspect that land degradation is endogenous, meaning that it is influenced by other factors that also affect food security. For example, farmers may be more likely to engage in unsustainable land use practices if they are facing economic pressures, or if they lack access to technology or education that would help them use the land more sustainably. To address this endogeneity, we can use an instrumental variable approach. We can use an instrumental variable that is correlated with land degradation but not directly with food security, and that does not affect food security through any other channels except through its impact on land degradation. For example, we could use rainfall as an instrumental variable, since it affects the extent of land degradation but does not directly affect food security. The idea is to use the variation in rainfall as an exogenous source of variation in land degradation that we can use to estimate its impact on food security. We can use panel IV estimation to estimate the following regression equation: FoodSecurity it = β_0 + β 1 LandDegradation it + β 2 CerealYield it + β3GHGE it + α i + u it where FoodSecurity it is the dependent variable (food security indicator metrix we used food availability) for country i at time t, LandDegradation it is the endogenous independent variable (land degradation measure) for country i at time t, GHGE it is the independent variable (GHGE measure) for country i at time t, (cereal yields measure) for country i at time t, α i is the country-specific fixed effect, and u it is the idiosyncratic error term. To address endogeneity, we need to find a valid instrumental variable for LandDegradation it . We therefore used rainfall as an instrument and estimate the first-stage regression: LandDegradation it = π_0 + π 1 Rainfall it + π 2 CerealYield it + π 3 GHGE it + α i + v it where Rainfall it is the instrumental variable for Rainfall it . The coefficient π 1 measures the effect of rainfall on land degradation, controlling for other factors that may affect land degradation. We can then use the predicted values of LandDegradation it from the first-stage regression as an instrument in the second-stage regression of FoodSecurity it on the predicted values of LandDegradation it , CerealYiely it , GHGE it , and the country-specific effect α i . 3.2.2. Panel OLS Linear regression models Following the work of (Feltran-Barbieri & Féres, 2021 ) (Affoh et al., 2022 ), (Ceesay, E. K. (2020)), (Ceesay et al., 2021 ), we estimated panel Ols as follows: Food production = β 0 + β 1 Arable land + β 2 Rainfall + β 3 fertilizer + β 4 Irrigation + α + u Where: Food production is the dependent variable as a proxy for food security Land Degradation is the independent variables (arable land as a proxy for land degradation), Rainfall, fertilizer and irrigation are all control variables α is the country-specific effect or unobserved factors, and u is the idiosyncratic error term. Pooled OLS Linear regression models The following the works of the following authors’, (Muir et al., 2023 ), Ceesay, E. ( 2020 ), (Ceesay et al., ( 2021 ), (Ceesay E., 2020 ). we estimated the Pooled OLS as follows; Food production = β 0 + β 1 Arable land + β 2 Rainfall + β 3 fertilizer + β 4 Irrigation + u We hypothesizing that arable land, Rainfall, fertilizer, and Irrigation effects on food security. The coefficients β 1 - β 4 represent the expected effects of these variables on food production, proxy for food security, controlling for other factors in the model. Hypothesis Testing H 0 : Land degradation is not correlated with food security. H 1 : Land degradation is correlated with food security. Notes: To estimate the model, we used econometric software such as Stata, R, matlab-econometric toolbox, and Eview 12. 4. Results Data Descriptions, Sources, Country and Definition of each variables used in this study The data used in this study is secondary data and was extracted from World Development indicator. There are 12 countries from sub-saharan Africa that are involved in this study to understand how land degradation affect food security and other control variables in this study. We calculated land productivity which a proxy variables for land degradation and we expected it to have to have either positive or negative signs depending on the quality of the soil in Sub Saharan African countries.Therefore, land productivity were calculated by divided total productivity over total land areas in order to have land degradation as a good proxy for land productivity. The sources and definitions of variables used in this study is as follows; Table xxxx: Variable names, Sources and definition or comments of the variables Variable Name Sources Definitions Food production WDI Food production, as the name suggests, is all about preparing food, in which raw materials are converted into ready-made food products for human use either in the home or in the food processing industries. Arable land (%land areas) WDI Arable land (from the Latin: arabilis, "able to be ploughed") is any land capable of being ploughed and used to grow crops. fertilizer consumption WDI Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients–animal and plant manures–are not included. the quantity of plant nutrients used per unit of arable land Agricultural irrigated land WDI Irrigated agricultural area refers to area equipped to provide water (via artificial means of irrigation such as by diverting streams, flooding, or spraying) to the crops. In non-irrigated agricultural areas, production of crops is dependent on rain-fed irrigation. average precipitation WDI Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country Land productivity WDI Agricultural output per unit of land. Greenhouse Gas Emission WDI Greenhouse gases (also known as GHGs) are gases in the earth's atmosphere that trap heat Cereal yields WDI Cereal yields mean harvested production per unit of harvested area for crop products ENDOGENEITY AND MULTICOLINEARITY TEST Multicollinearity does not bias the estimate; therefore, all the explanatory variables are comprised in the multilevel type of conventional logistic (see detailed explanation in the methodology). For all the households, 13 variables are found to have a positive correlation with household migration response status, and six are found to have a negative correlation with the migration status of the households (See detail in Table...). For example, income and migration are positively correlated (the correlation coefficient is about 20.2 percent). The variables from the bivariate analysis-Pearson product-moment correlation coefficients that have a tolerance value greater than 0.20 and variance inflation factor (VIF) lower than five after conducting a linear regression analysis followed by VIF will be further Analysis in a multilevel version of the logistic regression model. Those who passed the multicollinearity test remained exposed to further statistical Scrutiny; the multilevel version of the logistic regression model was employed to understand better their influence in determining household migration status in the rural Gambia. Table: Multicollinearity tests results Collinearity Statistics Endogenity test Variables Variance Inflation Factor(VIF) Tolerance Factor(1/VIF) F( 1, 223) = 0.00 Prob > F = 0.9475 Land productivity 1.42 0.702106 Land Area 1.48 0.676252 Land under cereal 1.62 0.616131 Total GHGE 4.68 0.213750 Average precipitation 1.04 0.960155 Cereal yield 4.06 0.246485 Conditions: VIF > 5 and 1/VIF < 0.20, Multicollinearity is present. Source: Own Evaluation Used WDI data If the tolerance value, i.e., 1/VIF, is below 0.20 and the Variance Inflation Factor (VIF) is greater than 5, multicollinearity is present. It is suitable for the VIF to lie amid 1–10. As shown in Table xx, none of the variables showed any multicollinearity signs. Finally, the model was estimated after checking multicollinearity issues.The output in the table above shows the result of an endogeneity test in a regression model. Null Hypothesis (H0): The variable is not endogenous, meaning it does not have a correlation with the error term in the regression model.Alternative Hypothesis (H1): The variable is endogenous, meaning it has a correlation with the error term in the regression model. The F-statistic for the test is 0.00. This statistic is used to determine whether the null hypothesis can be rejected. P-value (Prob > F): The p-value is 0.9475. This p-value indicates the probability of observing the test statistic, or one more extreme, under the null hypothesis. Since the p-value is 0.9475, which is significantly greater than conventional significance levels (e.g., 0.01, 0.05, 0.10), we fail to reject the null hypothesis. Therefore, based on this test, there is no evidence to suggest that food production is endogenous. In other words, residual of food does not appear to be correlated with the error term in the regression model, implying it is exogenous.In summary, the endogeneity test results indicate that the variable residual of food security is exogenous in your regression model, meaning you do not need to worry about endogeneity bias for this variable in your analysis. 4.1. Descriptive statistic and Correlation Descriptive statistics refers to the use of numerical and graphical methods to summarize and describe important features of a dataset. The purpose of descriptive statistics is to provide a clear and concise overview of the data, so that patterns and relationships within the data can be easily understood. However, source of the dataset is the World Development Indicators, and the years covered are from 2005 to 2021. On the other hand, Correlation is a statistical measure that describes the degree to which two or more variables are related or associated with each other. Correlation analysis can be used to explore the strength and direction of the relationship between two continuous variables. It measures the degree to which changes in one variable are associated with changes in another variable. Table 1 Descriptive statistic and correlation Variable Name Mean Std. d.. Minimum Maximum Correlation Food production 97.52 19.73 53.41 181.51 - Arable land (%land areas) 13.18 11.93 1.59 44.47 -0.18 fertilizer consumption 19.97 15.82 0.39 65.22 0.29 Agricultural irrigated land 1.03 0.76 0.39 2.26 0.86 average precipitation 591 251 250 900 -0.63 Land productivity 0.0013 0.0019 0.000036 0 .0095 0.24 Greenhouse Gas Emission 93589 141979 1528 560857 0.08 Cereal yields 1436 932 289 5331 0.25 Own evaluation using stata 16 This output shows summary statistics for five variables related to food production and agriculture. The first variable is Food production index (2014–2016 = 100), which measures the agricultural output of a country over a three-year period, with a base year of 2014–2016. The minimum value is 53.41 and the maximum value is 181.51, with a mean of 97.52 and a standard deviation of 19.73. The second variable is Arable land (% of land area), which measures the percentage of land that is suitable for agriculture. The minimum value is 1.59% and the maximum value is 44.47%, with a mean of 13.18% and a standard deviation of 11.93. The third variable is in this study is Fertilizer consumption (kilograms per hectare of arable land), which measures the amount of fertilizer used per unit of arable land. The minimum value is 0.39 kg/ha and the maximum value is 65.22 kg/ha, with a mean of 19.97 kg/ha and a standard deviation of 65.22. The fourth variable is Agricultural irrigated land (% of total agricultural land), which measures the percentage of agricultural land that is irrigated. The minimum value is 0.39% and the maximum value is 2.26%, with a mean of 1.03% and a standard deviation of 0.76. The fifth variable is Average precipitation in depth (mm per year), which measures the amount of rainfall in millimeters per year. The minimum value is 250 mm and the maximum value is 900 mm, with a mean of 591.13 mm and a standard deviation of 250.86.On the other hand, this is a correlation matrix between five variables: Food production index (2014–2016), Arable land (% of land area), Fertilizer consumption (kilograms per hectare of arable land), Agricultural irrigated land (% of total agricultural land), and Average precipitation (mm per year). The diagonal values represent the correlation of each variable with itself, which is always 1. The off-diagonal values represent the correlation between each pair of variables. For example, the correlation between Food production index and Arable land is -0.18, which means that there is a weak negative correlation between these two variables. Similarly, the correlation between Food production index and Agricultural irrigated land is 0.86, which means that there is a strong positive correlation between these two variables. The land productivity and cereal yield as positive correlation with food production. Land when is productivity it is less likely to be degraded and that is why crops grow into that land more effective and efficient.Overall, this correlation matrix can provide some insights into the relationships between these variables, but it is important to keep in mind that correlation does not necessarily imply causation. 4.2. Econometric results Table 2: Instrumemtal variable, when the dependent variable is food production. ivregress 2sls Foodproductionindex20142016 CerealyieldkgperhectareA (landproductivity = Averageprecipitationindepth Totalgreenhousegasemissions) Instrumental variables (2SLS) regression Number of obs = 228 Wald chi2(2) = 23.49 Prob > chi2 = 0.0000 R-squared = 0.0827 Root MSE = 21.619 Variable name Coef. Std. Err. z P-value Land productivity 5293.094 1855.778 2.85 0.004*** Cereal yield .0062933 .0016133 3.90 0.000*** Intercept term 68.15293 3.609603 18.88 0.000*** Note: *, ** and *** are statistically significant at 10 percent, 5 percent and 1 percent, respectively. Source: Authors’ calculation using Stata 16 for window. Instrumented: land productivity Instruments: Cereal yield kg per hectare A, Average precipitation in depth, Total greenhouse gas emissions When we used rainfall and GHG as an instrument for land productivity, we found that land productivity and cereal yields increases food productions. Thus, land productivity has positive significant effect on food production while controlling cereal yield. There is slightly positive significant influence of cereal yields on food production in selected sub-Saharan African countries. Thus, due to changes in rainfall and high co2 drives by GHG emission, pollution and other environmental damages from human activities are the major cause of poor cereal yields and that have pessimistic effect on food production in developing countries. Subsequently, cereal yield from land productivity have almost similar impact on food production in this region.1 unit rise in cereal yield and land productivity, food production rises by 25 and 24 percents respectively. Table 3 Multiple Linear regression used for the study between land degradation and food security in sub- saharan Africa, dependent variable food production as proxy for food security. Coef. Std. Err. t P-Value Arable land -0.55 0.85 -0.65 0.535 Fertilizer consumption 1.64 0.21 7.92 0.000*** Agricultural irrigated land 6.24 2.87 2.17 0.058** Average precipitation -0.08 0.010 -8.02 0.000*** Intercept terms 114.18 10.05 11.36 0.000*** Note: *, ** and *** are statistically significant at 10 percent, 5 percent and 1 percent, respectively. Source: Authors’ calculation using Stata 16 for window. The overall model is statistically significant, with a p-value of 0.0000. The R-squared value of 0.9748 indicates that the model explains a high percentage of the variation in the dependent variable (food production index). Among the independent variables, fertilizer consumption has a statistically significant positive relationship with the food production index, with p-values less than 0.05. Arable land does not have a statistically significant relationship with the food production index with p-values greater than 0.05 and the coefficient is negative while agricultural irrigated land is significant at 10 percent of alpha and have positive impact on food security. Therefore, based on this model, fertilizer consumption and agricultural irrigated land are the most important factors influencing food production index, while arable land and average precipitation do not have a significant impact. However, 1 unit increase in agriculture irrigated land in sub–Saharan African countries, food security increases by 6.24 percent respectively. Table 4 Panel OLS regression model under random-effects GLS regression analysis used for the study between land degradation and food security in sub- saharan Africa, dependent variable food production as proxy for food security using Xtreg command. Coef. Std. Err. z P-value Arable land -0.55 0.85 -0.65 0.519 Fertilizer consumption 1.63 0.21 7.92 0.000*** Agricultural irrigated land 6.24 2.87 2.17 0.030** Average precipitation -0.08 0.01 -8.02 0.000*** Intercept term 114.18 10.04 11.36 0.000*** Note: *, ** and *** are statistically significant at 10 percent, 5 percent and 1 percent, respectively. Source: Authors’ calculation using Stata 16 for window. Table 5 Variables included in this study Indicators Food production index (2014–2016 = 100) Arable land (% of land area) Average precipitation in depth (mm per year) Agricultural irrigated land (% of total agricultural land) Fertilizer consumption (% of fertilizer production) Land productivity Greenhouse emission Cereal yields Table 6 Country included in this study Year Country Country Country Country Country Country 2000 Burkina Faso Congo Cote d'Ivoire Ethiopia Gambia, The Mali 2001 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2002 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2003 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2004 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2005 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2006 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2007 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2008 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2009 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2010 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2011 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2012 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2013 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2014 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2015 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2016 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2017 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2018 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2019 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2020 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2021 Burkina Faso Congo, Dem. Rep. Cote d'Ivoire Ethiopia Gambia, The Mali 2000 Mauritania Niger Nigeria Senegal South Africa South Sudan 2001 Mauritania Niger Nigeria Senegal South Africa South Sudan 2002 Mauritania Niger Nigeria Senegal South Africa South Sudan 2003 Mauritania Niger Nigeria Senegal South Africa South Sudan 2004 Mauritania Niger Nigeria Senegal South Africa South Sudan 2005 Mauritania Niger Nigeria Senegal South Africa South Sudan 2006 Mauritania Niger Nigeria Senegal South Africa South Sudan 2007 Mauritania Niger Nigeria Senegal South Africa South Sudan 2008 Mauritania Niger Nigeria Senegal South Africa South Sudan 2009 Mauritania Niger Nigeria Senegal South Africa South Sudan 2010 Mauritania Niger Nigeria Senegal South Africa South Sudan 2011 Mauritania Niger Nigeria Senegal South Africa South Sudan 2012 Mauritania Niger Nigeria Senegal South Africa South Sudan 2013 Mauritania Niger Nigeria Senegal South Africa South Sudan 2014 Mauritania Niger Nigeria Senegal South Africa South Sudan 2015 Mauritania Niger Nigeria Senegal South Africa South Sudan 2016 Mauritania Niger Nigeria Senegal South Africa South Sudan 2017 Mauritania Niger Nigeria Senegal South Africa South Sudan 2018 Mauritania Niger Nigeria Senegal South Africa South Sudan 2019 Mauritania Niger Nigeria Senegal South Africa South Sudan 2020 Mauritania Niger Nigeria Senegal South Africa South Sudan 2021 Mauritania Niger Nigeria Senegal South Africa South Sudan Own compilation data from WDI In this model, we have used the same set of independent variables as the previous model. However, this time we have specified a random-effects GLS regression model using the panel ols commands in stata(xtreg). The results indicate that the Arable land variable is not statistically significant (p-value > 0.05) and has a negative coefficient (-0.55), suggesting that a one-unit increase in the proportion of arable land to total land area is associated with a decrease in the food production index by 0.55, but this relationship is not significant. On the other hand, the other two independent variables have statistically significant coefficients (p-value < 0.05) and positive coefficients, indicating that higher levels of fertilizer consumption, and agricultural irrigated land are all associated with higher levels of food production by 1.63, and 6.24 respectively. Moreover, average precipitation has negative significant influence on food production in sub–Saharan African countries. Due to changing nature of rainfall, 1 unit rise in rainfall, food security declines by 0.08 percent. Additionally, the random-effects model estimates a variance parameter for the unobserved country-level effects (sigma_u), which is zero in this case, indicating that there is no significant variation in the intercepts across countries. The variance parameter for the error term (sigma_e) is estimated to be 3.7985438. The rho parameter, which is the fraction of variance due to country-level effects, is estimated to be zero, suggesting that the variation in the intercepts is explained entirely by the individual-level variables included in the model. 5. Relevance When we testing for endogeneity of our instruments for land degradation in the model for food security, we found that the coefficient is significant at 5 percent level and we concluded that rainfall and GHG is significant instruments for land degradation. When we tested the overidentification, we found that our p-value of 0.3954 is greater than 0.05, suggests that the instruments selected are valid. Specifically, the Sargan test and the Basmann test. These tests are used to assess the validity of instrumental variables in an econometric model, particularly in the context of instrumental variable estimation like two-stage least squares (2SLS). The tests help determine whether the instrumental variables used in the model are valid instruments.From our results after ivregress 2sls, Sargan (score) test: chi2(1) = 0.722313, p = 0.3954. The Sargan test statistic is 0.722313, and it follows a chi-squared distribution with 1 degree of freedom (chi2(1)). The p-value associated with this test statistic is 0.3954. In the Sargan test, you're testing the null hypothesis that the instrumental variables are valid. A higher p-value (such as 0.3954), the better. Therefore, based on the Sargan test, there is strong evidence to suggest that the instrumental variables are valid. Basmann test: chi2(1) = 0.711896, p = 0.3988. The Basmann test statistic is 0.711896, and it also follows a chi-squared distribution with 1 degree of freedom (chi2(1)). The p-value associated with this test statistic is 0.3988.Likewise the Sargan test, the Basmann test is used to assess the validity of instrumental variables. The p-value of 0.3988 suggests that, based on the Basmann test, there is no strong evidence to reject the null hypothesis that the instrumental variables are invalid. In both cases, the relatively high p-values indicate that you have sufficient evidence to conclude that the instrumental variables are valid. In other words, the instrumental variables used in our econometric model appear to be valid based on the results of these tests. Finally, the empirical model's relevance lies in providing policymakers and stakeholders with quantitative insights into the linkages between land degradation and food security. It can inform evidence-based decision-making, support the development of targeted interventions for sustainable land management, and highlight the importance of addressing land degradation to achieve food security goals. 6. Discussion In the line with our hypothesis, in which we are dealing with whether land degradation is correlated with food security. When we used IV for land degradation, which is rainfall and GHG, we found that land productivity is optimistic impact on food security. This hypothesis is in line with Henri-ukoha, A. ( 2018 ), which found that land productivity increases due to soil management types and Barbera et al., 2012 found that when Using an experimental approach, we assumed that after 19 years of different land management techniques differences in the soil organic carbon can be detected. Further, we confirmed that cereal yields increase food production. (Dobermann and Cassman, 2005) also found that, gain in cereal yields will at least partly rely on increased nitrogen (N) and other inputs. The second regression without taking into account the panel structure of the data showed that the variables Fertilizer consumption significant predictors of Food production-proxy food security and this result confirmed in the study done by (Guo & Chen, 2022 ). But Fertilizer consumption has positive effect on food security while average precipitation has negative significant impact on food security. This is conformed in the study by (Ceesay & Ben Omar Ndiaye, 2022). However, the variable Arable land as a proxy of land degradation has negative insignificant effect on food security. This hypothesis is in line with the study ((Keringingo & Kayakayacı, 2023 ) and (Pozza & Field, 2020 ). We found that agriculture irrigated land has significant positive impact on food security. Furthermore, it mean that irrigation water supply to the land will boost crop production and animals rearing and that in turn will have substantial positive significant influence on food security in sub-Saharan Africa, the author elaborated. Agricultural irrigated land has significant positive predictors of Food production in sub Saharan African countries and this hypothesis also is in line with().It means that as land degraded, the fertility of soil decline and it causes food security declined, the author elaborated.The third regression you ran using the random-effects GLS model with panel data showed similar results. Again, Fertilizer consumption and "Average precipitation were significant predictors of Food production. However, in this case, "Agricultural irrigated land was also a significant predictor of Food production. This is confirmed in the study done by((Chalise et al., 2019 ), (Xie et al., 2018 ), (de Graaff et al., 2011 ) and (Macheroum & Chenchouni, 2022 ). 7. Conclusion and Policy Implication Based on the results, it can be concluded that the variables of Fertilizer consumption and Agricultural irrigated land are significant predictors of the Food production index in the given sub Saharan African countries selected under investigation. However, Arable land of land area is not found to be a significant predictor because we also noticed that if land is degraded it means it is infertile and it lead to food security problems due to crops cannot grow well into that particular land or forest. The variance inflation factor (VIF) values indicate that multicollinearity is not a concern in the model. Additionally, the random-effects GLS regression model shows that the within-group variation explains a large portion of the total variation in the Food production index. Overall, the results suggest that Fertilizer consumption, and Agricultural irrigated land, play a significant role in determining the Food production index in the sub Saharan African countries, and policymakers may consider these factors when developing strategies to improve food production. However, Based on the results of the regression analysis, it appears that fertilizer consumption and agricultural irrigated land are significant predictors of food production index, whereas arable land is not significant predictors. This suggests that policies aimed at promoting the efficient use of fertilizers and water resources could lead to increased food production. Moreover, governments and organizations could invest in agricultural extension services to educate farmers on the optimal use of fertilizers and irrigation, as well as promote the use of drought-tolerant crop varieties that require less water. Furthermore, policies that encourage the adoption of more sustainable agricultural practices, such as conservation agriculture and agroforestry, could also contribute to increased food production while mitigating the negative impact of farming on the environment. Overall, the results of this analysis suggest that promoting more sustainable and efficient agricultural practices, particularly with regard to fertilizer use and water management, could lead to increased food production and help address food security challenges in the long term. Limitation and future research on this study There are several limitations to this analysis that should be considered when interpreting the results. First, the data used in this analysis is limited to a small sample of countries and covers a short time period, which may not be representative of other countries or time periods. Second, this analysis only considers a limited number of factors that may affect food production, such as fertilizer consumption, arable land, agricultural irrigation, and precipitation. Other factors, such as pests and diseases, access to markets, and political instability, could also have a significant impact on food production. Third, this analysis only examines the relationship between the selected factors and food production, but it does not establish a causal relationship. It is possible that there are other factors that could be driving both food production and the selected factors, which could lead to a spurious correlation. Future research could address some of these limitations by using a larger sample size, including additional factors that could affect food production, and using more sophisticated statistical methods to establish causal relationships. Additionally, future research could focus on developing policies and interventions that aim to address the factors identified in this analysis as significant predictors of food production. Declarations Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. Author contributions MQ: conceptualization, methodology, writing—reviewing and editing. XL: formal analysis, methodology, and writing—original draft. SQ: methodology, validation, writing— reviewing and editing. GM: investigation and supervision. All authors contributed to the article and approved the submitted version. Funding This research was funded by the Qinhuangdao Social Science Development Research Project, grant number 2022LX024. Acknowledgments Thanks are given to my tutor for his guidance on this article, which greatly improved the quality of the article. Thank you for providing this academic platform for me to submit my manuscript. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Ceesay, E. K., & Ben Omar Ndiaye, M. (2022). Climate change, food security and economic growth nexus in the Gambia: Evidence from an econometrics analysis. Research in Globalization, 5(August), 100089. https://doi.org/10.1016/j.resglo.2022.100089 Ceesay, E. K., Francis, P. C., Jawneh, S., Njie, M., Belford, C., & Fanneh, M. M. (2021). Climate change, growth in agriculture value-added, food availability and economic growth nexus in the Gambia: a Granger causality and ARDL modeling approach. In SN Business & Economics (Vol. 1, Issue 7). https://doi.org/10.1007/s43546-021-00100-6 Chalise, D., Kumar, L., & Kristiansen, P. (2019). Land degradation by soil erosion in Nepal: A review. Soil Systems, 3(1), 1–18. https://doi.org/10.3390/soilsystems3010012 Employment in agriculture, migration, bilateral aids, economic growth and remittance: Evidence from the Gambia. Economics, Management and Sustainability, 5(1), 48–67. https://doi.org/10.14254/jems.2020.5-1.5 Keringingo, T., & Kayakayacı, Z. (2023). Agricultural Land Access and Use in Burundi. East African Scholars Journal of Agriculture and Life Sciences, 6(02), 42–53. https://doi.org/10.36349/easjals.2023.v06i02.002 Macheroum, A., & Chenchouni, H. (2022). Short-term land degradation driven by livestock grazing does not affect soil properties in semiarid steppe rangelands. Frontiers in Environmental Science, 10(September), 1–17. https://doi.org/10.3389/fenvs.2022.846045 Makurira, H. (2011). Rainfed Agriculture in Sub-Saharan Africa. Water Productivity in Rainfed Agriculture, 181(November 1947), 35–48. https://doi.org/10.1201/b10823-9 Midler, E. (2022). Environmental degradation: Impacts on agricultural production. Https://Ieep.Eu/Publications/Agriculture-and-Land-Management/Environmental-Degradation-Impacts-on-Agricultural-Production, 1996. Perspectives, A. (2023). Food Security and Safety Volume 2. In Food Security and Safety Volume 2 (Vol. 2). https://doi.org/10.1007/978-3-031-09614-3 UN. (2007). Water scarcity and desertification. UNCCD Thematic Fact Sheet Series, No. 2(2). UNCTAD. (2015). The Besieged Palestinian Agricultural Sector. United Nations Conference on Trade and Development, 1–49. https://unctad.org/en/PublicationsLibrary/gdsapp2015d1_en.pdf WHO. (2002). Section 2- Emergencies. Journal of Financial Economics, 49(July), 49–51. Chemical Releases Associated With. (n.d.). Hazbavi, Z., Sadeghi, S. H., Gholamalifard, M., & Davudirad, A. A. (2020). Watershed health assessment using the pressure–state–response (PSR) framework. Land Degradation and Development, 31(1), 3–19. https://doi.org/10.1002/ldr.3420 Wolfslehner, B., & Vacik, H. (2008). Evaluating sustainable forest management strategies with the Analytic Network Process in a Pressure-State-Response framework. Journal of Environmental Management, 88(1), 1–10. https://doi.org/10.1016/j.jenvman.2007.01.027 Liu, D., & Hao, S. (2017). Ecosystem health assessment at county-scale using the pressure-state-response framework on the loess plateau, China. International Journal of Environmental Research and Public Health, 14(1). https://doi.org/10.3390/ijerph14010002 Building a case for more public support BACKGROUND DOCUMENT. (2006). 01. de Graaff, J., Kessler, A., & Nibbering, J. W. (2011). Agriculture and food security in selected countries in Sub-Saharan Africa: Diversity in trends and opportunities. Food Security, 3(2), 195–213. https://doi.org/10.1007/s12571-011-0125-4 Xie, H., Perez, N., Anderson, W., Ringler, C., & You, L. (2018). Can Sub-Saharan Africa feed itself? The role of irrigation development in the region’s drylands for food security. Water International, 43(6), 796–814. https://doi.org/10.1080/02508060.2018.1516080 Pozza, L. E., & Field, D. J. (2020). The science of Soil Security and Food Security. Soil Security, 1(August), 100002. https://doi.org/10.1016/j.soisec.2020.100002 CSAO-CILSS. (2008). CSAO-CILSS:Food Security Profile, The Gambia. April, 1–27. Guo, J., & Chen, J. (2022). The Impact of Heavy Rainfall Variability on Fertilizer Application Rates: Evidence from Maize Farmers in China. International Journal of Environmental Research and Public Health, 19(23). https://doi.org/10.3390/ijerph192315906 Feltran-Barbieri, R., & Féres, J. G. (2021). Degraded pastures in Brazil: Improving livestock production and forest restoration. Royal Society Open Science, 8(7). https://doi.org/10.1098/rsos.201854 UN. (2007). Water scarcity and desertification. UNCCD Thematic Fact Sheet Series, No. 2(2). Ceesay, E. (2020). Employment in agriculture, migration, bilateral aids, economic growth and remittance: Evidence from the Gambia. Economics, Management and Sustainability, 5(1), 48–67. https://doi.org/10.14254/jems.2020.5-1.5 Affoh, R., Zheng, H., Dangui, K., & Dissani, B. M. (2022). The Impact of Climate Variability and Change on Food Security in Sub-Saharan Africa: Perspective from Panel Data Analysis. Sustainability (Switzerland), 14(2). https://doi.org/10.3390/su14020759 Muir, C., Smith, A. C., & Agrawal, A. (2023). Climate change, degradation, and land acquisitions: evaluating inequalities among competing interests for suitable cropland in Ethiopia. Ecology and Society, 28(1). https://doi.org/10.5751/ES-13934-280146 Weber, K. T., & Horst, S. (2011). Desertification and livestock grazing: The roles of sedentarization, mobility and rest. Pastoralism, 1(1), 1–11. https://doi.org/10.1186/2041-7136-1-19 Fusco, G. (2022). Climate Change and Food Security in the Northern and Eastern African Regions: A Panel Data Analysis. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912664 Hazbavi, Z., Sadeghi, S. H., Gholamalifard, M., & Davudirad, A. A. (2020). Watershed health assessment using the pressure–state–response (PSR) framework. Land Degradation and Development, 31(1), 3–19. https://doi.org/10.1002/ldr.3420 Wolfslehner, B., & Vacik, H. (2008). Evaluating sustainable forest management strategies with the Analytic Network Process in a Pressure-State-Response framework. Journal of Environmental Management, 88(1), 1–10. https://doi.org/10.1016/j.jenvman.2007.01.027 Building a case for more public support BACKGROUND DOCUMENT. (2006). 01. Liu, D., & Hao, S. (2017). Ecosystem health assessment at county-scale using the pressure-state-response framework on the loess plateau, China. International Journal of Environmental Research and Public Health, 14(1). https://doi.org/10.3390/ijerph14010002 Maxim, L., Spangenberg, J. H., & O’Connor, M. (2009). An analysis of risks for biodiversity under the DPSIR framework. Ecological Economics, 69(1), 12–23. https://doi.org/10.1016/j.ecolecon.2009.03.017 Henri-ukoha, A. (2018). LAND PRODUCTIVITY OF DIFFERENT USE LEVELS OF SUSTAINABLE SOIL MANAGEMENT TECHNIQUES OF ARABLE CROP FARMERS IN IMO STATE , LAND PRODUCTIVITY OF DIFFERENT USE LEVELS OF SUSTAINABLE SOIL MANAGEMENT. October. Barbera, V., Poma, I., Gristina, L., Novara, A., & Egli, M. (2012). LONG-TERM CROPPING SYSTEMS AND TILLAGE MANAGEMENT EFFECTS ON SOIL ORGANIC CARBON STOCK AND STEADY STATE LEVEL OF C SEQUESTRATION RATES IN A SEMIARID ENVIRONMENT. 91(October 2010), 82–91. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4709288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333891169,"identity":"05dd9e03-cb7a-44d5-a03a-63c8219d6e4b","order_by":0,"name":"Ebrima K. Ceesay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBCDBAZmBgbGhgogk5m5gRQtZ0BaGInVAgSMjW1gCr8W/vbmwx9+ttnkmbdzJ36cOa82mr8dqOVHxTacWiTOHEuT7G1LK5Y5zLtZcuO247kzDjM2MPacuY1Ti4FEjhkDb9vhxBnMvBskH247ltsA1MLM2IZHi/z7zx//tv0Hadn88+GcY7nzCWqR4GGQ5m07ANKyTXJjQ03uBkJaJM6kmUnLnEsulgBqsZxx7EDuRqCWg/j8wt9++PHHN2V2eRL8Zzff7Kmpy513/vDBBz8qcGsBA0Y2OPMwmDyAXz0I/IGz6ggrHgWjYBSMghEHAEh1XuuZF2CvAAAAAElFTkSuQmCC","orcid":"","institution":"University of the Gambia","correspondingAuthor":true,"prefix":"","firstName":"Ebrima","middleName":"K.","lastName":"Ceesay","suffix":""},{"id":333891171,"identity":"468a5dc0-208f-4d48-8fae-a53f9659c751","order_by":1,"name":"Kadio Valere","email":"","orcid":"","institution":"Cheikh Anta Diop University","correspondingAuthor":false,"prefix":"","firstName":"Kadio","middleName":"","lastName":"Valere","suffix":""},{"id":333891173,"identity":"2f8f2af6-1e00-402b-a5fa-69597fa495f4","order_by":2,"name":"Alasana Gitteh","email":"","orcid":"","institution":"University of the Gambia","correspondingAuthor":false,"prefix":"","firstName":"Alasana","middleName":"","lastName":"Gitteh","suffix":""},{"id":333891175,"identity":"49e5c601-fca9-4d9f-b962-c6a9549d3328","order_by":3,"name":"Mohamed Ben Omar Ndiaye","email":"","orcid":"","institution":"Cheikh Anta Diop University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Ben Omar","lastName":"Ndiaye","suffix":""},{"id":333891176,"identity":"f55afc06-c402-4cb5-8ed3-613bce035d45","order_by":4,"name":"Amadou Cham","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Amadou","middleName":"","lastName":"Cham","suffix":""}],"badges":[],"createdAt":"2024-07-09 05:27:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4709288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4709288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61864494,"identity":"94248265-61bd-4390-a26c-389b51f5c722","added_by":"auto","created_at":"2024-08-06 11:54:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170870,"visible":true,"origin":"","legend":"\u003cp\u003eArea of tree covered areas in Africa.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/b027d8f8d81e32067e7901cc.png"},{"id":61864490,"identity":"a1e3111e-918c-42cb-99e8-99269ca4a536","added_by":"auto","created_at":"2024-08-06 11:54:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66614,"visible":true,"origin":"","legend":"\u003cp\u003eArea of terrestrial barren land by continent.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/00c92b2f00f84fde461816d6.png"},{"id":61864489,"identity":"9561d240-7d0c-4801-b447-f4ce943db57e","added_by":"auto","created_at":"2024-08-06 11:54:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119209,"visible":true,"origin":"","legend":"\u003cp\u003eAfrica plus total Area by land cover class\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/3a05fb162d4a7c50189512bf.png"},{"id":61865546,"identity":"caaf7158-10b0-419b-838d-05f5a455e9bf","added_by":"auto","created_at":"2024-08-06 12:02:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71812,"visible":true,"origin":"","legend":"\u003cp\u003eArea of tree covered area by continent\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/1c9aec6d15ad4cdf7dfa7390.png"},{"id":61865547,"identity":"7ded224a-fff8-4938-9254-78c1348ee74a","added_by":"auto","created_at":"2024-08-06 12:02:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":112617,"visible":true,"origin":"","legend":"\u003cp\u003eWorld plus total area by land cover class\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/16ba14d3eec54c916cbb04c5.png"},{"id":61864492,"identity":"5bb2ca10-41de-4b99-9e53-6e51fde2707a","added_by":"auto","created_at":"2024-08-06 11:54:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22603,"visible":true,"origin":"","legend":"\u003cp\u003ePanel Graph for cereal yields.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/651702950d793ec4a78a83a7.png"},{"id":61865545,"identity":"8f63d3a8-b376-46b2-afc9-1b854f849ef9","added_by":"auto","created_at":"2024-08-06 12:02:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":26720,"visible":true,"origin":"","legend":"\u003cp\u003ePanel Graph for food availability in Sub-saharan Africa.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/a7d386609714f2c247a41ceb.png"},{"id":80822249,"identity":"448550eb-cf0e-441a-86be-f915268ff08b","added_by":"auto","created_at":"2025-04-17 12:23:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1661411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4709288/v1/deeb8a3a-9c81-4e14-a056-c05ea39f7de6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land degradation and food security nexus in Sub Saharan Africa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLand degradation is defined as \u0026quot;a negative trend in land conditions caused by direct or indirect human-induced processes, including anthropogenic climate change, expressed as long-term reduction or loss of at least one of the following aspects: biological productivity, ecological integrity, or value to humans\u0026quot; (IPCC, 2019). Another definition by FAO, land degradation is defined as a change in the soil health status resulting in a diminished capacity of the ecosystem to provide goods and services for its beneficiaries (FAO, 2014). Hunger: craving or urgent need for food or a specific nutrient. In other word, an uneasy sensation occasioned by the lack of food, Finally, hunger is a weakened condition brought about by prolonged lack of food (https://www.merriam-webster.com/dictionary/hunger) Hunger is an uncomfortable or painful physical sensation caused by insufficient consumption of dietary energy( FAO,2024, (World Bank, 1986)) (Clay, 2002).\u003c/p\u003e\n\u003cp\u003eFood security: exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food favorites for an energetic and healthy lifetime (World Food Summit 1996) (FAO 2008).\u003c/p\u003e\n\u003cp\u003eFood insecurity : A person is food insecure when they lack regular access to enough safe and nutritious food for normal growth and development and an active and healthy life. This may be due to unavailability of food and/or lack of resources to obtain food(FAO, 2020).\u003c/p\u003e\n\u003cp\u003eLand degradation poses a significant challenge to agricultural productivity and food availability in Sub-Saharan Africa. Depending on the extent of the degradation, it could experience a temporary or permanent decline in its ability to support the human food supply (Gupta, 2019). Indeed, if land fertility is more pronounced, food supply disruption will become more frequent posing the issue of food insecurity in Sub-saharan Africa.\u003c/p\u003e\n\u003cp\u003eAccording to (https://www.worldvision.org/hunger-news-stories/africa, 2022, FAO 2022), an estimated 20% of the population is undernourished in Africa, with 57 million more people facing hunger since the start of the COVID-19 pandemic. Further, they estimated 868 million people experienced moderate to severe food insecurity in Africa in 2022, with over one-third of those facing severe food insecurity\u003c/p\u003e\n\u003cp\u003eAccording to World Bank, 2022, at least one in five Africans goes to bed hungry and an estimated 140 million people in Africa face acute food insecurity, according to the 2022 Global Report on Food Crises 2022 Mid-Year Update.\u003c/p\u003e\n\u003cp\u003eAccording to WFP, 2024, Nearly 55 million people in West and Central Africa will struggle to feed themselves in the June-August 2024 lean season, according to the March 2024 Cadre Harmonis\u0026eacute; food security analysis released by the Permanent Inter-State Committee for Drought Control in the Sahel (CILSS).\u003c/p\u003e\n\u003cp\u003eThis are the mains problems of Africa according to (Wijnand Klaver, 2015).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ehigh population growth rates (a burden because the cake has to be divided among more people);\u003c/li\u003e\n \u003cli\u003ea young population (which could potentially offer an economic dividend in the coming decades);\u003c/li\u003e\n \u003cli\u003ea high percentage of food produced by women (many of whom are poor but very resourceful);\u003c/li\u003e\n \u003cli\u003ea highly diverse ecology (which poses particular challenges in terms of an agricultural \u0026lsquo;revolution\u0026rsquo;);\u003c/li\u003e\n \u003cli\u003ean increase in potential and current conflicts around competing claims (e.g. pastoralists versus farmers, food versus biofuel, water for food versus water for export flowers);\u003c/li\u003e\n \u003cli\u003emicroclimates and climate change (which will lead to certain areas becoming \u0026lsquo;bread baskets\u0026rsquo; and negatively impact on others that will become virtually uninhabitable);\u003c/li\u003e\n \u003cli\u003eevolving institutional and governance structures; and\u003c/li\u003e\n \u003cli\u003ecertain food habits (shaped by tradition but strongly influenced by cosmopolitan trends).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAccording to IPBES (2018), it is caused by natural and anthropogenic direct drivers, which are influenced by indirect drivers. As natural causes driving land degradation, we can quote earthquakes, volcanic eruptions, hurricanes, floods, landslides, typhoons, and recurring outbreaks of pests and pathogens (IPBES, 2018). They happen episodically with periodicities ranging from years to millennia (Angaman \u0026amp; Niang, 2023). Concerning the anthropogenic direct causes, they are directly linked to human activities and range from local to regional or global scales posing serious threat to food security.\u003c/p\u003e\n\u003cp\u003eAccording to D\u0026iacute;az et al. (2015), indirect drivers represent the fundamental root causes of land degradation. These drivers originate from how human societies operate, organize themselves, and engage with nature across various levels. Indirect drivers are typically external to the specific ecosystem being studied. As example, IPBES (2018) quotes demographic factors, economic activities, sciences, knowledge and technology, institutions and governance, and cultural aspect as indirect drivers of land degradation.\u003c/p\u003e\n\u003cp\u003eLand degradation has a significant impact on food security. It reduces agricultural productivity, decreases crop quality, and limits access to arable land. It reduces also water availability and increases the prevalence of invasive species which are harmful to the growth of crops (Abdeta \u0026amp; Geleto, 2018). In this regard, land degradation should be stop in order to have good soil to cultivate crops for effective food production. Education, changes in policies, use of technology for meaningful innovation are vital to be considered to restoration of degraded land and future land degradation. The fertility of the soil is greatly diminished by land degradation, resulting in reduced agricultural productivity. Soil erosion, nutrient depletion, and the loss of soil organic matter impair the land\u0026apos;s capacity to support crop growth, leading to decreased yields and availability of food. Several studies, such as those conducted by Chalise et al. (2019), Ceesay \u0026amp; Ben Omar Ndiaye (2022), and Perspectives (2023), have confirmed this correlation.\u003c/p\u003e\n\u003cp\u003eAnother consequence of land degradation is the reduced access to arable land. As land becomes degraded, farmers face limitations in finding suitable land for cultivation, which hinders their ability to produce enough food for themselves and their communities. The works of Makurira (2011), UNCTAD (2015), and Keringingo \u0026amp; Kayakayacı (2023) provide support for this claim.Livestock production and grazing areas are also affected by land degradation. Degraded pastures lead to a shortage of fodder for livestock, negatively impacting their health and productivity. As a result, the availability of animal-sourced food products such as meat and dairy is diminished, further contributing to food insecurity. This is contrary to the findings of Weber \u0026amp; Horst (2011), Ceesay et al. (2021), Feltran-Barbieri \u0026amp; F\u0026eacute;res (2021), and Macheroum \u0026amp; Chenchouni (2022). Water resources are adversely affected by land degradation as well. Decreased water holding capacity of soils, increased runoff, and reduced groundwater recharge contribute to water scarcity and poor water quality.\u003c/p\u003e\n\u003cp\u003eThese factors hamper agricultural activities, limit irrigation, and lead to lower crop yields and food production. Studies conducted by WHO (2002), UN (2007), and Chemical Releases Associated With (n.d.) support these findings. Land degradation can also initiate a negative feedback loop, whereby degraded land contributes to climate change and climate variability. In turn, climate change exacerbates land degradation through more frequent and intense droughts, floods, and extreme weather events, further compromising agricultural productivity and food security. Similar conclusions have been drawn in the study by Midler (2022). Understanding the multifaceted relationship between land degradation and food security is crucial for devising comprehensive strategies to address these challenges. By considering the various impacts described above, policymakers and stakeholders can work towards sustainable land management practices that ensure long-term food security for Sub-Saharan African countries and beyond.\u003c/p\u003e\n\u003cp\u003eResearch question: What is the impact of land degradation on food security in Sub Saharan Africa?\u003c/p\u003e\n\u003cp\u003eObjective: The overall objective of the empirical model is to examine the relationship between land degradation and food security indicators, such as crop yield, land productivity, GHGE (greenhouse gas emission), food production, and rainfall.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eOne theoretical model that has been used to analyze the impact of land degradation on food security is the Pressure-State-Response (PSR) framework and the Driver-Pressure-State-Impact-Response (DPSIR). This model proposes that human activities create pressures on the environment, which affect the state of the environment, and then lead to responses from society or the government. In the case of land degradation and food security, human activities such as deforestation, overgrazing, and excessive use of chemical fertilizers create pressures on the land, which can result in soil erosion, loss of fertility, and reduced crop yields. These changes in the state of the land can then lead to responses from society, such as reduced access to food, increased food prices, and food insecurity (Liu \u0026amp; Hao, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); (Wolfslehner \u0026amp; Vacik, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and (Hazbavi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) while The Driver-Pressure-State-Impact-Response (DPSIR) was promoted to show the cause\u0026ndash;effect relationships between environmental and human activities. The framework was introduced in a report by Maxim, L., Spangenberg, J. H., \u0026amp; O\u0026rsquo;Connor, M. (2009) to help policy makers to understand the meaning of the information in indicator reports. Thus, PSR is related to DPSIR in a number of ways according to the following authors; For PSR, society then responds to these changes on the environment by instituting environmental and economic programmes and policies, which feedback to reduce or mitigate the pressures or repair the natural resource such as mining, energy etc. Thus, for DPSIR framework was developed in the late 1990s and proposed by the Organisation of Economic Co-operation and Development (OECD, 2003) as a means of structuring and organizing indicators that affect and causes the pressures on environment due to human activities such as pollution, plastic that affect the environment, fossil fuel energy etc in a way that is meaningful to decision makers. Overall, DSPIR was built on previous environmental frameworks, such as the Pressure-State-Response (PSR) (OECD, 1993) and the Driver-State-Response (DSR) (UN, 1996) to understand the effect-cause relationship of PSR. Subsequently, there is a significant amount of empirical literature on the impact of land degradation on food security. Many studies have used panel data analysis to examine the relationship between land degradation and food security, controlling for other factors such as agricultural inputs, infrastructure, and economic growth. For instance, a study by Asfaw et al. (2019) investigated the impact of soil erosion on food security in Ethiopia using panel data analysis. The study found that soil erosion significantly reduced food production, and the negative effect was more severe in areas with poor soil quality. Similarly, a study by Demeke et al. (2018) examined the relationship between land degradation and food security in Sub-Saharan Africa using panel data analysis. The study found that land degradation had a significant negative impact on food security, and suggested that efforts to combat land degradation could lead to improved food security.Other studies have also used econometric models to examine the impact of specific types of land degradation, such as desertification or deforestation, on food security. For example, a study by Bai et al. (2018) examined the impact of desertification on food security in China using a structural equation model. The study found that desertification had a significant negative impact on food security, and suggested that policies aimed at preventing desertification could help improve food security in the region.\u003c/p\u003e \u003cp\u003eIn most literature the countries selected by the Dutch government for development cooperation in the coming years, namely Benin, Burundi, Ethiopia, Ghana, Kenya, Mali, Mozambique, Rwanda, Sudan and Uganda. Those countries have fertilie land for agriculture, but still no developmet in agriculture and food security taking place and even in the rest of the African countries all have fertile land but still lower budget assigned to agriculture especially in the Gambia almost only 2 percent of the total budget for 2020.\u003c/p\u003e \u003cp\u003eOverall, empirical studies provide robust evidence that land degradation has a negative impact on food security, and highlight the importance of sustainable land management practices to improve food security.One gap in the literature could be the lack of studies that investigate the impact of specific types of land degradation on food security. Many studies have focused on the overall impact of land degradation on food security, but there is a need for research that examines the effects of different types of land degradation such as soil erosion, desertification, and deforestation on food production and access to food. Additionally, there is a need for studies that explore the underlying mechanisms through which land degradation affects food security, as this could inform more targeted policy interventions to address the issue.The paper will explore the panel FIV estimations or this study by using econometrics approaches. The aim of this paper is to examine the links between land dagradation anc food security in selected African countries and the implications on the stability of the country in particular and Africa in general.The study is rarely seeing in which Africa as it will used more efficient panel IV model with indication of food production as a proxy for food security and land degradation dicnators such as productivity of the land.. This study is very significant in order to allow policy decision in both international and Africa stakeholders especially the channels of envrionmental management, agricultural sector on the implications of the rising food insecurity and hunger in the Africa over the time period.\u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cp\u003eData Collection: We Gathered data on land degradation indicators-land productivity, land cover change, and food security indicators- cereal yields, food production-food availability proxy, at the appropriate spatial and temporal scales. All data gathered are secondary data from FAOSTAT and WDI.\u003c/p\u003e \u003cp\u003eVariable Selection: We identify the key variables that represent land degradation and food security and control variables. These variables include Food production index (2014\u0026ndash;2016\u0026thinsp;=\u0026thinsp;100), Average precipitation, land productivity, GHGE (greenhouse gas emission), Irrigation, Arable land, and fertilizer consumption.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Econometric Specifications\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Instrumental variables estimation (IV)\u003c/h2\u003e \u003cp\u003ePanel IV estimation is a statistical technique used to estimate the causal relationship between an endogenous variable, dependent variable and a set of exogenous, independent variables and instrumental variables in a panel dataset. It is useful when there is worry about endogeneity and omitted variable bias.\u003c/p\u003e \u003cp\u003eThe general formula for panel IV estimation is:\u003c/p\u003e \u003cp\u003eY\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;βX\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γZ\u003csub\u003eit\u003c/sub\u003e + U\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003eY\u003csub\u003eit\u003c/sub\u003e is the dependent variable for individual i at time t\u003c/p\u003e \u003cp\u003eX\u003csub\u003eit\u003c/sub\u003e is the endogenous explanatory variable for individual i at time t\u003c/p\u003e \u003cp\u003eZ\u003csub\u003eit\u003c/sub\u003e is a set of exogenous and instrumental variables for individual i at time t\u003c/p\u003e \u003cp\u003eα, β, and γ are the intercept, coefficient of the endogenous variable, and coefficients of the exogenous and instrumental variables, respectively\u003c/p\u003e \u003cp\u003eU\u003csub\u003eit\u003c/sub\u003e is the error term for individual i at time t\u003c/p\u003e \u003cp\u003eTo estimate this model using panel IV, we use the following steps:\u003c/p\u003e \u003cp\u003eCheck for the presence of endogeneity and omitted variable bias.\u003c/p\u003e \u003cp\u003eChoose appropriate instruments for the endogenous variable.\u003c/p\u003e \u003cp\u003eEstimate the first stage regression to obtain the predicted values of the endogenous variable.\u003c/p\u003e \u003cp\u003eCheck for the validity of the instruments by testing for the exogeneity of the instruments and the relevance of the first stage regression.\u003c/p\u003e \u003cp\u003eEstimate the second stage regression using the predicted values of the endogenous variable and the exogenous and instrumental variables.The IV estimator provides consistent and unbiased estimates of the coefficients, and the Sargan test can be used to test for the validity of the instruments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Panel OLS regression model\u003c/h2\u003e \u003cp\u003eThe panel OLS regression equation can be written as:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;u\u003c/p\u003e \u003cp\u003eWhere Y is the dependent variable, X is the independent variable, α is the individual-specific effect, and u is the idiosyncratic error term.\u003c/p\u003e \u003cp\u003eThe individual-specific effect captures any unobserved factors (α\u003csub\u003ei\u003c/sub\u003e) that are constant over time for each individual, such as innate ability or family background. The idiosyncratic error term captures any factors that are specific to each observation, such as measurement error or random shocks (u).To estimate the coefficients of the regression equation, we can use ordinary least squares (OLS) on the pooled dataset, which combines all the observations for all individuals and all time periods. The OLS estimator gives us the best linear unbiased estimates of the coefficients (BLUE).One way to interpret the estimated coefficient β\u003csub\u003e1\u003c/sub\u003e is as the effect of land degradation on food security, controlling for individual-specific factors and idiosyncratic factors. We can use this coefficient to predict the change in food security associated with a one-unit increase in land degradation, on average, across all individuals and all time periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Pooled OLS Linear regression models\u003c/h2\u003e \u003cp\u003eThe pooled ols is divided into simple and multiple linear regression frameworks as:\u003c/p\u003e \u003cp\u003eSimple Linear Regression: The simple linear regression model examines the relationship between a dependent variable (Y) and a single independent variable (X).\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eX\u0026thinsp;+\u0026thinsp;ε\u003c/p\u003e \u003cp\u003eIn this equation:\u003c/p\u003e \u003cp\u003eY represents the dependent variable, which is the variable to be predicted or explained.\u003c/p\u003e \u003cp\u003eX represents the independent variable, which is the variable used to predict or explain the dependent variable.\u003c/p\u003e \u003cp\u003eβ\u003csub\u003e0\u003c/sub\u003e is the y-intercept, which represents the value of Y when X is zero.\u003c/p\u003e \u003cp\u003eΒ\u003csub\u003e1\u003c/sub\u003e is the slope coefficient, which represents the change in Y associated with a one-unit change in X.\u003c/p\u003e \u003cp\u003eε is the error term, which accounts for the variability in Y that is not explained by the independent variable.\u003c/p\u003e \u003cp\u003eMultiple regression analysis: is a statistical technique used to estimate the relationship between a dependent variable and multiple independent variables. The estimation process involves determining the coefficients of the independent variables that best fit the data and provide the best prediction of the dependent variable. The estimation in multiple regression analysis is typically done using the method of ordinary least squares (OLS).\u003c/p\u003e \u003cp\u003eSpecify the regression model: Start by specifying the regression model, including the dependent variable and the independent variables. The model is expressed as:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β₀ + β₁X₁ + β₂X₂ + ...\u0026thinsp;+\u0026thinsp;βₖXₖ + ε\u003c/p\u003e \u003cp\u003eIn this equation:\u003c/p\u003e \u003cp\u003eY is the dependent variable.\u003c/p\u003e \u003cp\u003eX₁, X₂, ..., Xₖ are the independent variables.\u003c/p\u003e \u003cp\u003eβ₀, β₁, β₂, ..., βₖ are the coefficients to be estimated and as Y changes, what happen to 1 unit changes in Xs\u0026rsquo;.\u003c/p\u003e \u003cp\u003eε is the error term.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Empirical Model Specification:\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. IV\u003c/h2\u003e \u003cp\u003eWe suspect that land degradation is endogenous, meaning that it is influenced by other factors that also affect food security. For example, farmers may be more likely to engage in unsustainable land use practices if they are facing economic pressures, or if they lack access to technology or education that would help them use the land more sustainably. To address this endogeneity, we can use an instrumental variable approach. We can use an instrumental variable that is correlated with land degradation but not directly with food security, and that does not affect food security through any other channels except through its impact on land degradation. For example, we could use rainfall as an instrumental variable, since it affects the extent of land degradation but does not directly affect food security. The idea is to use the variation in rainfall as an exogenous source of variation in land degradation that we can use to estimate its impact on food security.\u003c/p\u003e \u003cp\u003eWe can use panel IV estimation to estimate the following regression equation:\u003c/p\u003e \u003cp\u003eFoodSecurity\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β_0\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eLandDegradation\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eCerealYield\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β3GHGE\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;u\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere FoodSecurity\u003csub\u003eit\u003c/sub\u003e is the dependent variable (food security indicator metrix we used food availability) for country i at time t, LandDegradation\u003csub\u003eit\u003c/sub\u003e is the endogenous independent variable (land degradation measure) for country i at time t, GHGE\u003csub\u003eit\u003c/sub\u003e is the independent variable (GHGE measure) for country i at time t, (cereal yields measure) for country i at time t, α\u003csub\u003ei\u003c/sub\u003e is the country-specific fixed effect, and u\u003csub\u003eit\u003c/sub\u003e is the idiosyncratic error term.\u003c/p\u003e \u003cp\u003eTo address endogeneity, we need to find a valid instrumental variable for LandDegradation\u003csub\u003eit\u003c/sub\u003e. We therefore used rainfall as an instrument and estimate the first-stage regression:\u003c/p\u003e \u003cp\u003eLandDegradation\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;π_0\u0026thinsp;+\u0026thinsp;π\u003csub\u003e1\u003c/sub\u003eRainfall\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;π\u003csub\u003e2\u003c/sub\u003eCerealYield\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;π\u003csub\u003e3\u003c/sub\u003e GHGE\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;v\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere Rainfall\u003csub\u003eit\u003c/sub\u003e is the instrumental variable for Rainfall\u003csub\u003eit\u003c/sub\u003e. The coefficient π\u003csub\u003e1\u003c/sub\u003e measures the effect of rainfall on land degradation, controlling for other factors that may affect land degradation. We can then use the predicted values of LandDegradation\u003csub\u003eit\u003c/sub\u003e from the first-stage regression as an instrument in the second-stage regression of FoodSecurity\u003csub\u003eit\u003c/sub\u003e on the predicted values of LandDegradation\u003csub\u003eit\u003c/sub\u003e, CerealYiely\u003csub\u003eit\u003c/sub\u003e, GHGE\u003csub\u003eit\u003c/sub\u003e, and the country-specific effect α\u003csub\u003ei\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Panel OLS Linear regression models\u003c/h2\u003e \u003cp\u003eFollowing the work of (Feltran-Barbieri \u0026amp; F\u0026eacute;res, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Affoh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), (Ceesay, E. K. (2020)), (Ceesay et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we estimated panel Ols as follows:\u003c/p\u003e \u003cp\u003eFood production\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eArable land\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eRainfall\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003efertilizer\u0026thinsp;+\u0026thinsp;β\u003csub\u003e4\u003c/sub\u003eIrrigation\u0026thinsp;+\u0026thinsp;α\u0026thinsp;+\u0026thinsp;u\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eFood production is the dependent variable as a proxy for food security\u003c/p\u003e \u003cp\u003eLand Degradation is the independent variables (arable land as a proxy for land degradation),\u003c/p\u003e \u003cp\u003eRainfall, fertilizer and irrigation are all control variables\u003c/p\u003e \u003cp\u003eα is the country-specific effect or unobserved factors,\u003c/p\u003e \u003cp\u003eand u is the idiosyncratic error term.\u003c/p\u003e \u003cp\u003ePooled OLS Linear regression models\u003c/p\u003e \u003cp\u003eThe following the works of the following authors\u0026rsquo;, (Muir et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Ceesay, E. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Ceesay et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), (Ceesay E., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). we estimated the Pooled OLS as follows;\u003c/p\u003e \u003cp\u003eFood production\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eArable land\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eRainfall\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003efertilizer\u0026thinsp;+\u0026thinsp;β\u003csub\u003e4\u003c/sub\u003eIrrigation + u\u003c/p\u003e \u003cp\u003eWe hypothesizing that arable land, Rainfall, fertilizer, and Irrigation effects on food security. The coefficients β\u003csub\u003e1\u003c/sub\u003e- β\u003csub\u003e4\u003c/sub\u003e represent the expected effects of these variables on food production, proxy for food security, controlling for other factors in the model.\u003c/p\u003e \u003cp\u003eHypothesis Testing\u003c/p\u003e \u003cp\u003eH\u003csub\u003e0\u003c/sub\u003e: Land degradation is not correlated with food security.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e: Land degradation is correlated with food security.\u003c/p\u003e \u003cp\u003eNotes: To estimate the model, we used econometric software such as Stata, R, matlab-econometric toolbox, and Eview 12.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003e \u003cb\u003eData Descriptions, Sources, Country and Definition of each variables used in this study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe data used in this study is secondary data and was extracted from World Development indicator. There are 12 countries from sub-saharan Africa that are involved in this study to understand how land degradation affect food security and other control variables in this study. We calculated land productivity which a proxy variables for land degradation and we expected it to have to have either positive or negative signs depending on the quality of the soil in Sub Saharan African countries.Therefore, land productivity were calculated by divided total productivity over total land areas in order to have land degradation as a good proxy for land productivity. The sources and definitions of variables used in this study is as follows;\u003c/p\u003e \u003cp\u003eTable xxxx: Variable names, Sources and definition or comments of the variables\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefinitions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFood production, as the name suggests, is all about preparing food, in which raw materials are converted into ready-made food products for human use either in the home or in the food processing industries.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArable land (%land areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land (from the Latin: arabilis, \"able to be ploughed\") is\u0026nbsp;any land capable of being ploughed and used to grow crops.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efertilizer consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients\u0026ndash;animal and plant manures\u0026ndash;are not included. the quantity of plant nutrients used per unit of arable land\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural irrigated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIrrigated agricultural area refers to area equipped to provide water (via artificial means of irrigation such as by diverting streams, flooding, or spraying) to the crops. In non-irrigated agricultural areas, production of crops is dependent on rain-fed irrigation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaverage precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage precipitation is the long-term average in depth (over space and time) of annual precipitation in the country\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural output per unit of land.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenhouse Gas Emission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenhouse gases (also known as GHGs) are\u0026nbsp;gases in the earth's atmosphere that trap heat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereal yields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereal yields mean\u0026nbsp;harvested production per unit of harvested area for crop products\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eENDOGENEITY AND MULTICOLINEARITY TEST\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMulticollinearity does not bias the estimate; therefore, all the explanatory variables are comprised in the multilevel type of conventional logistic (see detailed explanation in the methodology). For all the households, 13 variables are found to have a positive correlation with household migration response status, and six are found to have a negative correlation with the migration status of the households (See detail in Table...). For example, income and migration are positively correlated (the correlation coefficient is about 20.2 percent). The variables from the bivariate analysis-Pearson product-moment correlation coefficients that have a tolerance value greater than 0.20 and variance inflation factor (VIF) lower than five after conducting a linear regression analysis followed by VIF will be further Analysis in a multilevel version of the logistic regression model. Those who passed the multicollinearity test remained exposed to further statistical Scrutiny; the multilevel version of the logistic regression model was employed to understand better their influence in determining household migration status in the rural Gambia.\u003c/p\u003e \u003cp\u003eTable: Multicollinearity tests results\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEndogenity test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariance Inflation Factor(VIF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTolerance Factor(1/VIF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF( 1, 223) = 0.00\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F = 0.9475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.702106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.676252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand under cereal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.616131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal GHGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.213750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.960155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereal yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eConditions: VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 and 1/VIF\u0026thinsp;\u0026lt;\u0026thinsp;0.20, Multicollinearity is present. Source: Own Evaluation Used WDI data\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIf the tolerance value, i.e., 1/VIF, is below 0.20 and the Variance Inflation Factor (VIF) is greater than 5, multicollinearity is present. It is suitable for the VIF to lie amid 1\u0026ndash;10. As shown in Table xx, none of the variables showed any multicollinearity signs. Finally, the model was estimated after checking multicollinearity issues.The output in the table above shows the result of an endogeneity test in a regression model. Null Hypothesis (H0): The variable is not endogenous, meaning it does not have a correlation with the error term in the regression model.Alternative Hypothesis (H1): The variable is endogenous, meaning it has a correlation with the error term in the regression model. The F-statistic for the test is 0.00. This statistic is used to determine whether the null hypothesis can be rejected. P-value (Prob\u0026thinsp;\u0026gt;\u0026thinsp;F): The p-value is 0.9475. This p-value indicates the probability of observing the test statistic, or one more extreme, under the null hypothesis. Since the p-value is 0.9475, which is significantly greater than conventional significance levels (e.g., 0.01, 0.05, 0.10), we fail to reject the null hypothesis. Therefore, based on this test, there is no evidence to suggest that food production is endogenous. In other words, residual of food does not appear to be correlated with the error term in the regression model, implying it is exogenous.In summary, the endogeneity test results indicate that the variable residual of food security is exogenous in your regression model, meaning you do not need to worry about endogeneity bias for this variable in your analysis.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Descriptive statistic and Correlation\u003c/h2\u003e \u003cp\u003eDescriptive statistics refers to the use of numerical and graphical methods to summarize and describe important features of a dataset. The purpose of descriptive statistics is to provide a clear and concise overview of the data, so that patterns and relationships within the data can be easily understood. However, source of the dataset is the World Development Indicators, and the years covered are from 2005 to 2021. On the other hand, Correlation is a statistical measure that describes the degree to which two or more variables are related or associated with each other. Correlation analysis can be used to explore the strength and direction of the relationship between two continuous variables. It measures the degree to which changes in one variable are associated with changes in another variable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistic and correlation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. d..\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArable land (%land areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efertilizer consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural irrigated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaverage precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 .0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenhouse Gas Emission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e560857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereal yields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eOwn evaluation using stata 16\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis output shows summary statistics for five variables related to food production and agriculture. The first variable is Food production index (2014\u0026ndash;2016\u0026thinsp;=\u0026thinsp;100), which measures the agricultural output of a country over a three-year period, with a base year of 2014\u0026ndash;2016. The minimum value is 53.41 and the maximum value is 181.51, with a mean of 97.52 and a standard deviation of 19.73. The second variable is Arable land (% of land area), which measures the percentage of land that is suitable for agriculture. The minimum value is 1.59% and the maximum value is 44.47%, with a mean of 13.18% and a standard deviation of 11.93. The third variable is in this study is Fertilizer consumption (kilograms per hectare of arable land), which measures the amount of fertilizer used per unit of arable land. The minimum value is 0.39 kg/ha and the maximum value is 65.22 kg/ha, with a mean of 19.97 kg/ha and a standard deviation of 65.22. The fourth variable is Agricultural irrigated land (% of total agricultural land), which measures the percentage of agricultural land that is irrigated. The minimum value is 0.39% and the maximum value is 2.26%, with a mean of 1.03% and a standard deviation of 0.76. The fifth variable is Average precipitation in depth (mm per year), which measures the amount of rainfall in millimeters per year. The minimum value is 250 mm and the maximum value is 900 mm, with a mean of 591.13 mm and a standard deviation of 250.86.On the other hand, this is a correlation matrix between five variables: Food production index (2014\u0026ndash;2016), Arable land (% of land area), Fertilizer consumption (kilograms per hectare of arable land), Agricultural irrigated land (% of total agricultural land), and Average precipitation (mm per year). The diagonal values represent the correlation of each variable with itself, which is always 1. The off-diagonal values represent the correlation between each pair of variables. For example, the correlation between Food production index and Arable land is -0.18, which means that there is a weak negative correlation between these two variables. Similarly, the correlation between Food production index and Agricultural irrigated land is 0.86, which means that there is a strong positive correlation between these two variables. The land productivity and cereal yield as positive correlation with food production. Land when is productivity it is less likely to be degraded and that is why crops grow into that land more effective and efficient.Overall, this correlation matrix can provide some insights into the relationships between these variables, but it is important to keep in mind that correlation does not necessarily imply causation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Econometric results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;2: Instrumemtal variable, when the dependent variable is food production.\u003c/p\u003e \u003cp\u003eivregress 2sls Foodproductionindex20142016 CerealyieldkgperhectareA (landproductivity\u0026thinsp;=\u0026thinsp;Averageprecipitationindepth Totalgreenhousegasemissions)\u003c/p\u003e \u003cp\u003eInstrumental variables (2SLS) regression\u003c/p\u003e \u003cp\u003eNumber of obs\u0026thinsp;=\u0026thinsp;228\u003c/p\u003e \u003cp\u003eWald chi2(2)\u0026thinsp;=\u0026thinsp;23.49\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.0000\u003c/p\u003e \u003cp\u003eR-squared\u0026thinsp;=\u0026thinsp;0.0827\u003c/p\u003e \u003cp\u003eRoot MSE\u0026thinsp;=\u0026thinsp;21.619\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5293.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1855.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereal yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0062933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0016133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.15293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.609603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: *, ** and *** are statistically significant at 10 percent, 5 percent and 1 percent, respectively. Source: Authors\u0026rsquo; calculation using Stata 16 for window.\u003c/p\u003e \u003cp\u003eInstrumented: land productivity\u003c/p\u003e \u003cp\u003eInstruments: Cereal yield kg per hectare A, Average precipitation in depth, Total greenhouse gas emissions\u003c/p\u003e \u003cp\u003eWhen we used rainfall and GHG as an instrument for land productivity, we found that land productivity and cereal yields increases food productions. Thus, land productivity has positive significant effect on food production while controlling cereal yield. There is slightly positive significant influence of cereal yields on food production in selected sub-Saharan African countries. Thus, due to changes in rainfall and high co2 drives by GHG emission, pollution and other environmental damages from human activities are the major cause of poor cereal yields and that have pessimistic effect on food production in developing countries. Subsequently, cereal yield from land productivity have almost similar impact on food production in this region.1 unit rise in cereal yield and land productivity, food production rises by 25 and 24 percents respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear regression used for the study between land degradation and food security in sub- saharan Africa, dependent variable food production as proxy for food security.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizer consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural irrigated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept terms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, ** and *** are statistically significant at 10 percent, 5 percent and 1 percent, respectively. Source: Authors\u0026rsquo; calculation using Stata 16 for window.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe overall model is statistically significant, with a p-value of 0.0000. The R-squared value of 0.9748 indicates that the model explains a high percentage of the variation in the dependent variable (food production index). Among the independent variables, fertilizer consumption has a statistically significant positive relationship with the food production index, with p-values less than 0.05. Arable land does not have a statistically significant relationship with the food production index with p-values greater than 0.05 and the coefficient is negative while agricultural irrigated land is significant at 10 percent of alpha and have positive impact on food security. Therefore, based on this model, fertilizer consumption and agricultural irrigated land are the most important factors influencing food production index, while arable land and average precipitation do not have a significant impact. However, 1 unit increase in agriculture irrigated land in sub\u0026ndash;Saharan African countries, food security increases by 6.24 percent respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePanel OLS regression model under random-effects GLS regression analysis used for the study between land degradation and food security in sub- saharan Africa, dependent variable food production as proxy for food security using Xtreg command.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizer consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural irrigated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, ** and *** are statistically significant at 10 percent, 5 percent and 1 percent, respectively. Source: Authors\u0026rsquo; calculation using Stata 16 for window.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables included in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood production index (2014\u0026ndash;2016\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArable land (% of land area)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage precipitation in depth (mm per year)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural irrigated land (% of total agricultural land)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizer consumption (% of fertilizer production)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand productivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenhouse emission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereal yields\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCountry included in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCongo, Dem. Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGambia, The\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Sudan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOwn compilation data from WDI\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this model, we have used the same set of independent variables as the previous model. However, this time we have specified a random-effects GLS regression model using the panel ols commands in stata(xtreg). The results indicate that the Arable land variable is not statistically significant (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and has a negative coefficient (-0.55), suggesting that a one-unit increase in the proportion of arable land to total land area is associated with a decrease in the food production index by 0.55, but this relationship is not significant. On the other hand, the other two independent variables have statistically significant coefficients (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and positive coefficients, indicating that higher levels of fertilizer consumption, and agricultural irrigated land are all associated with higher levels of food production by 1.63, and 6.24 respectively. Moreover, average precipitation has negative significant influence on food production in sub\u0026ndash;Saharan African countries. Due to changing nature of rainfall, 1 unit rise in rainfall, food security declines by 0.08 percent. Additionally, the random-effects model estimates a variance parameter for the unobserved country-level effects (sigma_u), which is zero in this case, indicating that there is no significant variation in the intercepts across countries. The variance parameter for the error term (sigma_e) is estimated to be 3.7985438. The rho parameter, which is the fraction of variance due to country-level effects, is estimated to be zero, suggesting that the variation in the intercepts is explained entirely by the individual-level variables included in the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Relevance","content":"\u003cp\u003eWhen we testing for endogeneity of our instruments for land degradation in the model for food security, we found that the coefficient is significant at 5 percent level and we concluded that rainfall and GHG is significant instruments for land degradation. When we tested the overidentification, we found that our p-value of 0.3954 is greater than 0.05, suggests that the instruments selected are valid. Specifically, the Sargan test and the Basmann test. These tests are used to assess the validity of instrumental variables in an econometric model, particularly in the context of instrumental variable estimation like two-stage least squares (2SLS). The tests help determine whether the instrumental variables used in the model are valid instruments.From our results after ivregress 2sls, Sargan (score) test: chi2(1)\u0026thinsp;=\u0026thinsp;0.722313, p\u0026thinsp;=\u0026thinsp;0.3954. The Sargan test statistic is 0.722313, and it follows a chi-squared distribution with 1 degree of freedom (chi2(1)). The p-value associated with this test statistic is 0.3954. In the Sargan test, you're testing the null hypothesis that the instrumental variables are valid. A higher p-value (such as 0.3954), the better. Therefore, based on the Sargan test, there is strong evidence to suggest that the instrumental variables are valid. Basmann test: chi2(1)\u0026thinsp;=\u0026thinsp;0.711896, p\u0026thinsp;=\u0026thinsp;0.3988. The Basmann test statistic is 0.711896, and it also follows a chi-squared distribution with 1 degree of freedom (chi2(1)). The p-value associated with this test statistic is 0.3988.Likewise the Sargan test, the Basmann test is used to assess the validity of instrumental variables. The p-value of 0.3988 suggests that, based on the Basmann test, there is no strong evidence to reject the null hypothesis that the instrumental variables are invalid. In both cases, the relatively high p-values indicate that you have sufficient evidence to conclude that the instrumental variables are valid. In other words, the instrumental variables used in our econometric model appear to be valid based on the results of these tests. Finally, the empirical model's relevance lies in providing policymakers and stakeholders with quantitative insights into the linkages between land degradation and food security. It can inform evidence-based decision-making, support the development of targeted interventions for sustainable land management, and highlight the importance of addressing land degradation to achieve food security goals.\u003c/p\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eIn the line with our hypothesis, in which we are dealing with whether land degradation is correlated with food security. When we used IV for land degradation, which is rainfall and GHG, we found that land productivity is optimistic impact on food security. This hypothesis is in line with Henri-ukoha, A. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which found that land productivity increases due to soil management types and Barbera et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e found that when Using an experimental approach, we assumed that after 19 years of different land management techniques differences in the soil organic carbon can be detected. Further, we confirmed that cereal yields increase food production. (Dobermann and Cassman, 2005) also found that, gain in cereal yields will at least partly rely on increased nitrogen (N) and other inputs. The second regression without taking into account the panel structure of the data showed that the variables Fertilizer consumption significant predictors of Food production-proxy food security and this result confirmed in the study done by (Guo \u0026amp; Chen, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). But Fertilizer consumption has positive effect on food security while average precipitation has negative significant impact on food security. This is conformed in the study by (Ceesay \u0026amp; Ben Omar Ndiaye, 2022). However, the variable Arable land as a proxy of land degradation has negative insignificant effect on food security. This hypothesis is in line with the study ((Keringingo \u0026amp; Kayakayacı, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and (Pozza \u0026amp; Field, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We found that agriculture irrigated land has significant positive impact on food security. Furthermore, it mean that irrigation water supply to the land will boost crop production and animals rearing and that in turn will have substantial positive significant influence on food security in sub-Saharan Africa, the author elaborated. Agricultural irrigated land has significant positive predictors of Food production in sub Saharan African countries and this hypothesis also is in line with().It means that as land degraded, the fertility of soil decline and it causes food security declined, the author elaborated.The third regression you ran using the random-effects GLS model with panel data showed similar results. Again, Fertilizer consumption and \"Average precipitation were significant predictors of Food production. However, in this case, \"Agricultural irrigated land was also a significant predictor of Food production. This is confirmed in the study done by((Chalise et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), (Xie et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), (de Graaff et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and (Macheroum \u0026amp; Chenchouni, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"7. Conclusion and Policy Implication","content":"\u003cp\u003eBased on the results, it can be concluded that the variables of Fertilizer consumption and Agricultural irrigated land are significant predictors of the Food production index in the given sub Saharan African countries selected under investigation. However, Arable land of land area is not found to be a significant predictor because we also noticed that if land is degraded it means it is infertile and it lead to food security problems due to crops cannot grow well into that particular land or forest. The variance inflation factor (VIF) values indicate that multicollinearity is not a concern in the model. Additionally, the random-effects GLS regression model shows that the within-group variation explains a large portion of the total variation in the Food production index. Overall, the results suggest that Fertilizer consumption, and Agricultural irrigated land, play a significant role in determining the Food production index in the sub Saharan African countries, and policymakers may consider these factors when developing strategies to improve food production. However, Based on the results of the regression analysis, it appears that fertilizer consumption and agricultural irrigated land are significant predictors of food production index, whereas arable land is not significant predictors. This suggests that policies aimed at promoting the efficient use of fertilizers and water resources could lead to increased food production. Moreover, governments and organizations could invest in agricultural extension services to educate farmers on the optimal use of fertilizers and irrigation, as well as promote the use of drought-tolerant crop varieties that require less water. Furthermore, policies that encourage the adoption of more sustainable agricultural practices, such as conservation agriculture and agroforestry, could also contribute to increased food production while mitigating the negative impact of farming on the environment. Overall, the results of this analysis suggest that promoting more sustainable and efficient agricultural practices, particularly with regard to fertilizer use and water management, could lead to increased food production and help address food security challenges in the long term.\u003c/p\u003e \u003cp\u003eLimitation and future research on this study\u003c/p\u003e \u003cp\u003eThere are several limitations to this analysis that should be considered when interpreting the results. First, the data used in this analysis is limited to a small sample of countries and covers a short time period, which may not be representative of other countries or time periods. Second, this analysis only considers a limited number of factors that may affect food production, such as fertilizer consumption, arable land, agricultural irrigation, and precipitation. Other factors, such as pests and diseases, access to markets, and political instability, could also have a significant impact on food production. Third, this analysis only examines the relationship between the selected factors and food production, but it does not establish a causal relationship. It is possible that there are other factors that could be driving both food production and the selected factors, which could lead to a spurious correlation. Future research could address some of these limitations by using a larger sample size, including additional factors that could affect food production, and using more sophisticated statistical methods to establish causal relationships. Additionally, future research could focus on developing policies and interventions that aim to address the factors identified in this analysis as significant predictors of food production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions MQ: conceptualization, methodology, writing\u0026mdash;reviewing and editing. XL: formal analysis, methodology, and writing\u0026mdash;original draft. SQ: methodology, validation, writing\u0026mdash; reviewing and editing. GM: investigation and supervision. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding This research was funded by the Qinhuangdao Social Science Development Research Project, grant number 2022LX024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments Thanks are given to my tutor for his guidance on this article, which greatly improved the quality of the article. Thank you for providing this academic platform for me to submit my manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePublisher\u0026rsquo;s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCeesay, E. K., \u0026amp; Ben Omar Ndiaye, M. (2022). Climate change, food security and economic growth nexus in the Gambia: Evidence from an econometrics analysis. Research in Globalization, 5(August), 100089. https://doi.org/10.1016/j.resglo.2022.100089\u003c/li\u003e\n\u003cli\u003eCeesay, E. K., Francis, P. C., Jawneh, S., Njie, M., Belford, C., \u0026amp; Fanneh, M. M. (2021). Climate change, growth in agriculture value-added, food availability and economic growth nexus in the Gambia: a Granger causality and ARDL modeling approach. In SN Business \u0026amp; Economics (Vol. 1, Issue 7). https://doi.org/10.1007/s43546-021-00100-6\u003c/li\u003e\n\u003cli\u003eChalise, D., Kumar, L., \u0026amp; Kristiansen, P. (2019). Land degradation by soil erosion in Nepal: A review. Soil Systems, 3(1), 1\u0026ndash;18. https://doi.org/10.3390/soilsystems3010012\u003c/li\u003e\n\u003cli\u003eEmployment in agriculture, migration, bilateral aids, economic growth and remittance: Evidence from the Gambia. Economics, Management and Sustainability, 5(1), 48\u0026ndash;67. https://doi.org/10.14254/jems.2020.5-1.5\u003c/li\u003e\n\u003cli\u003eKeringingo, T., \u0026amp; Kayakayacı, Z. (2023). Agricultural Land Access and Use in Burundi. East African Scholars Journal of Agriculture and Life Sciences, 6(02), 42\u0026ndash;53. https://doi.org/10.36349/easjals.2023.v06i02.002\u003c/li\u003e\n\u003cli\u003eMacheroum, A., \u0026amp; Chenchouni, H. (2022). Short-term land degradation driven by livestock grazing does not affect soil properties in semiarid steppe rangelands. Frontiers in Environmental Science, 10(September), 1\u0026ndash;17. https://doi.org/10.3389/fenvs.2022.846045\u003c/li\u003e\n\u003cli\u003eMakurira, H. (2011). Rainfed Agriculture in Sub-Saharan Africa. Water Productivity in Rainfed Agriculture, 181(November 1947), 35\u0026ndash;48. https://doi.org/10.1201/b10823-9\u003c/li\u003e\n\u003cli\u003eMidler, E. (2022). Environmental degradation: Impacts on agricultural production. Https://Ieep.Eu/Publications/Agriculture-and-Land-Management/Environmental-Degradation-Impacts-on-Agricultural-Production, 1996.\u003c/li\u003e\n\u003cli\u003ePerspectives, A. (2023). Food Security and Safety Volume 2. In Food Security and Safety Volume 2 (Vol. 2). https://doi.org/10.1007/978-3-031-09614-3\u003c/li\u003e\n\u003cli\u003eUN. (2007). Water scarcity and desertification. UNCCD Thematic Fact Sheet Series, No. 2(2).\u003c/li\u003e\n\u003cli\u003eUNCTAD. (2015). The Besieged Palestinian Agricultural Sector. United Nations Conference on Trade and Development, 1\u0026ndash;49. https://unctad.org/en/PublicationsLibrary/gdsapp2015d1_en.pdf\u003c/li\u003e\n\u003cli\u003eWHO. (2002). Section 2- Emergencies. Journal of Financial Economics, 49(July), 49\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eChemical Releases Associated With. (n.d.).\u003c/li\u003e\n\u003cli\u003eHazbavi, Z., Sadeghi, S. H., Gholamalifard, M., \u0026amp; Davudirad, A. A. (2020). Watershed health assessment using the pressure\u0026ndash;state\u0026ndash;response (PSR) framework. Land Degradation and Development, 31(1), 3\u0026ndash;19. https://doi.org/10.1002/ldr.3420\u003c/li\u003e\n\u003cli\u003eWolfslehner, B., \u0026amp; Vacik, H. (2008). Evaluating sustainable forest management strategies with the Analytic Network Process in a Pressure-State-Response framework. Journal of Environmental Management, 88(1), 1\u0026ndash;10. https://doi.org/10.1016/j.jenvman.2007.01.027\u003c/li\u003e\n\u003cli\u003eLiu, D., \u0026amp; Hao, S. (2017). Ecosystem health assessment at county-scale using the pressure-state-response framework on the loess plateau, China. International Journal of Environmental Research and Public Health, 14(1). https://doi.org/10.3390/ijerph14010002\u003c/li\u003e\n\u003cli\u003eBuilding a case for more public support BACKGROUND DOCUMENT. (2006). 01.\u003c/li\u003e\n\u003cli\u003ede Graaff, J., Kessler, A., \u0026amp; Nibbering, J. W. (2011). Agriculture and food security in selected countries in Sub-Saharan Africa: Diversity in trends and opportunities. Food Security, 3(2), 195\u0026ndash;213. https://doi.org/10.1007/s12571-011-0125-4\u003c/li\u003e\n\u003cli\u003eXie, H., Perez, N., Anderson, W., Ringler, C., \u0026amp; You, L. (2018). Can Sub-Saharan Africa feed itself? The role of irrigation development in the region\u0026rsquo;s drylands for food security. Water International, 43(6), 796\u0026ndash;814. https://doi.org/10.1080/02508060.2018.1516080\u003c/li\u003e\n\u003cli\u003ePozza, L. E., \u0026amp; Field, D. J. (2020). The science of Soil Security and Food Security. Soil Security, 1(August), 100002. https://doi.org/10.1016/j.soisec.2020.100002\u003c/li\u003e\n\u003cli\u003eCSAO-CILSS. (2008). CSAO-CILSS:Food Security Profile, The Gambia. April, 1\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eGuo, J., \u0026amp; Chen, J. (2022). The Impact of Heavy Rainfall Variability on Fertilizer Application Rates: Evidence from Maize Farmers in China. International Journal of Environmental Research and Public Health, 19(23). https://doi.org/10.3390/ijerph192315906\u003c/li\u003e\n\u003cli\u003eFeltran-Barbieri, R., \u0026amp; F\u0026eacute;res, J. G. (2021). Degraded pastures in Brazil: Improving livestock production and forest restoration. Royal Society Open Science, 8(7). https://doi.org/10.1098/rsos.201854\u003c/li\u003e\n\u003cli\u003eUN. (2007). Water scarcity and desertification. UNCCD Thematic Fact Sheet Series, No. 2(2).\u003c/li\u003e\n\u003cli\u003eCeesay, E. (2020). Employment in agriculture, migration, bilateral aids, economic growth and remittance: Evidence from the Gambia. Economics, Management and Sustainability, 5(1), 48\u0026ndash;67. https://doi.org/10.14254/jems.2020.5-1.5\u003c/li\u003e\n\u003cli\u003eAffoh, R., Zheng, H., Dangui, K., \u0026amp; Dissani, B. M. (2022). The Impact of Climate Variability and Change on Food Security in Sub-Saharan Africa: Perspective from Panel Data Analysis. Sustainability (Switzerland), 14(2). https://doi.org/10.3390/su14020759\u003c/li\u003e\n\u003cli\u003eMuir, C., Smith, A. C., \u0026amp; Agrawal, A. (2023). Climate change, degradation, and land acquisitions: evaluating inequalities among competing interests for suitable cropland in Ethiopia. Ecology and Society, 28(1). https://doi.org/10.5751/ES-13934-280146\u003c/li\u003e\n\u003cli\u003eWeber, K. T., \u0026amp; Horst, S. (2011). Desertification and livestock grazing: The roles of sedentarization, mobility and rest. Pastoralism, 1(1), 1\u0026ndash;11. https://doi.org/10.1186/2041-7136-1-19\u003c/li\u003e\n\u003cli\u003eFusco, G. (2022). Climate Change and Food Security in the Northern and Eastern African Regions: A Panel Data Analysis. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912664\u003c/li\u003e\n\u003cli\u003eHazbavi, Z., Sadeghi, S. H., Gholamalifard, M., \u0026amp; Davudirad, A. A. (2020). Watershed health assessment using the pressure\u0026ndash;state\u0026ndash;response (PSR) framework. Land Degradation and Development, 31(1), 3\u0026ndash;19. https://doi.org/10.1002/ldr.3420\u003c/li\u003e\n\u003cli\u003eWolfslehner, B., \u0026amp; Vacik, H. (2008). Evaluating sustainable forest management strategies with the Analytic Network Process in a Pressure-State-Response framework. Journal of Environmental Management, 88(1), 1\u0026ndash;10. https://doi.org/10.1016/j.jenvman.2007.01.027\u003c/li\u003e\n\u003cli\u003eBuilding a case for more public support BACKGROUND DOCUMENT. (2006). 01.\u003c/li\u003e\n\u003cli\u003eLiu, D., \u0026amp; Hao, S. (2017). Ecosystem health assessment at county-scale using the pressure-state-response framework on the loess plateau, China. International Journal of Environmental Research and Public Health, 14(1). https://doi.org/10.3390/ijerph14010002\u003c/li\u003e\n\u003cli\u003eMaxim, L., Spangenberg, J. H., \u0026amp; O\u0026rsquo;Connor, M. (2009). An analysis of risks for biodiversity under the DPSIR framework. Ecological Economics, 69(1), 12\u0026ndash;23. https://doi.org/10.1016/j.ecolecon.2009.03.017\u003c/li\u003e\n\u003cli\u003eHenri-ukoha, A. (2018). LAND PRODUCTIVITY OF DIFFERENT USE LEVELS OF SUSTAINABLE SOIL MANAGEMENT TECHNIQUES OF ARABLE CROP FARMERS IN IMO STATE , LAND PRODUCTIVITY OF DIFFERENT USE LEVELS OF SUSTAINABLE SOIL MANAGEMENT. October.\u003c/li\u003e\n\u003cli\u003eBarbera, V., Poma, I., Gristina, L., Novara, A., \u0026amp; Egli, M. (2012). LONG-TERM CROPPING SYSTEMS AND TILLAGE MANAGEMENT EFFECTS ON SOIL ORGANIC CARBON STOCK AND STEADY STATE LEVEL OF C SEQUESTRATION RATES IN A SEMIARID ENVIRONMENT. 91(October 2010), 82\u0026ndash;91.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Instrumental variables, Sub-Saharan Africa, Panel OLS, Land productivity, Greenhouse gas emission","lastPublishedDoi":"10.21203/rs.3.rs-4709288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4709288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e This article explores the relationship between land degradation and food security in Sub-Saharan African countries, shedding light on the critical issues faced in the region. Land degradation, caused by factors such as poor rainfall, deforestation, erosion, and other major causes, significantly impacts the fertility of the soil, leading to food security challenges. Understanding the impact of desertification, poor rainfall, drought, and extreme climate change in Africa is crucial to addressing food security problems in the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThe study utilizes data from the World Development Indicators and employs instrumental variable estimation (IV), panel OLS and pooled OLS methods to analyze the relationship between food production (as a proxy for food security) and various independent variables, including arable land area, fertilizer consumption, agricultural irrigated land area, and average precipitation depth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe findings reveal three different types of regression analyses. The first analysis was to Instrumental variable estimation (IV). When we used rainfall and GHG as an instrument for land productivity, proxy land degradation, we found that land productivity and cereal yields increases food productions, proxy food security. The second analysis, a random-effects Generalizing least square regression, indicates that fertilizer consumption and average precipitation depth are significant predictors of food production. However, arable land area and agricultural irrigated land area do not significantly impact food production. Interestingly, agricultural irrigated land shows a positive effect on food security in Sub-Saharan African countries, while arable land (as a proxy for land degradation) has a negative impact on food security in the region. The third analysis, a multiple linear regression, supports the results of the Generalizing least square regression, demonstrating that fertilizer consumption and average precipitation depth are significant predictors of food production. However, arable land area do not significantly influence food production. Remarkably, agricultural irrigated land is found to be a positive predictor of food production and serves as a proxy for food security.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003e In conclusion, this study highlights the detrimental impact of land degradation on food security in Sub-Saharan African countries. It emphasizes the significance of factors such as fertilizer consumption, land productivity-proxy land degradation, cereal yields, Greenhouse gas emission, average precipitation depth, and the role of agricultural irrigated land in addressing food security challenges in the region.\u003c/p\u003e","manuscriptTitle":"Land degradation and food security nexus in Sub Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-06 11:54:52","doi":"10.21203/rs.3.rs-4709288/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a1ba0c0-e0ee-4a32-8a3b-4d94a22fe7a0","owner":[],"postedDate":"August 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-17T12:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-06 11:54:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4709288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4709288","identity":"rs-4709288","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}